The global machine learning in pharmaceutical industry market was valued at USD 2.34 Billion in 2025 and is expected to grow at a CAGR of 37.70%, reaching USD 57.35 Billion by 2035. The market is driven by the rising demand for predictive analytics, drug discovery acceleration, and personalized medicine across the globe.
Base Year
Historical Period
Forecast Period
AI-driven software tools will remain central to pharmaceutical R&D, accelerating timelines and reducing discovery costs significantly.
One of the major market trends include growing regulatory support for AI integration, that will streamline approvals, boosting adoption across drug development and clinical trial processes.
The market growth is driven by the rising focus on data security and trial transparency will drive demand for ML-based anonymization and authentication platforms globally.
Compound Annual Growth Rate
37.7%
Value in USD Billion
2026-2035
*this image is indicative*
The pharmaceutical industry is leveraging machine learning algorithms to streamline R&D pipelines by predicting molecule behaviour, identifying drug targets, and optimizing compound design. A June 2024 study by MIT scientists introduced a new ML-based workflow capable of rapidly narrowing thousands of molecules down to promising drug candidates by predicting both target binding and safety profiles early in the process. The model was tested across four therapeutic target programs, where it reduced lead-optimization time by 40-50% and significantly decreased the number of compounds needing synthesis and testing. In one case, the model identified 100 viable compounds out of 10,000 possibilities, vastly improving development efficiency. This breakthrough highlights the growing role of machine learning in reducing cost, accelerating timelines, and improving decision-making across the pharmaceutical pipeline, from discovery to clinical translation. The market is anticipated to grow at a CAGR of 37.70% during the forecast period of 2026-2035.
Healthcare AI Market Growth Acceleration Through Authentication Innovation
Regulatory alignment and anti-counterfeiting concerns are driving the integration of AI-based authentication, while digital health adoption is enhancing supply chain transparency. For instance, in March 2025, Systech launched an AI-powered authentication solution for pharmaceuticals, enabling real-time verification of legitimate drugs via smartphone scanning and blockchain integration. This development bolsters market development by reinforcing regulatory compliance, mitigating counterfeiting risk, and increasing confidence in digital health ecosystems, fueling greater demand for machine learning solutions in pharmaceutical supply chains.
Major market trends include increasing adoption of generative AI along with rising use of machine learning in developing personalized dosing models and strategies, among others.
Generative AI powered Launch Excellence Enhancing Market Performance
Investment in generative AI tools is being propelled by demand for faster time-to-market and robust multi-channel stakeholder engagement, particularly for specialty-care therapies. For instance, in April 2024, IQVIA introduced its GenAI-powered Launch Cockpit, designed to analyze terabytes of structured and unstructured information, generate content, and provide real-time scenario planning. This innovation strengthens competitive positioning by delivering personalized launch strategies, improving decision-making efficiency, and ultimately shortening launch timelines.
Scientific ML and Biosimulation Fuelled Global Machine Learning in Pharmaceutical Industry Development
Continued demand for predictive dosing models and personalized dosing strategies supports investment in scientific ML tools. For instance, in October 2024, PumasAI launched DeepPumas and AskPumas, providing secure private data integration, curated biomedical knowledge, and advanced workflow automation tailored to pharmacometricians. These tools enhance scientific value by optimizing dosing regimens, reducing model hallucination risk, and increasing productivity, ultimately fostering higher market innovation and differentiation.
Software to Lead the Market Segmentation by Offering
The software segment is likely to hold the largest share in the market due to its central role in data processing, predictive modelling, and drug discovery algorithms. Machine learning software is essential for simulating molecular interactions, optimizing clinical trial designs, and supporting regulatory submissions. According to a 2024 study published in NPJ Digital Medicine, AI-driven software tools reduced early drug development timelines by up to 70%. These cost-saving, scalable solutions have wider adoption than services, making software the dominant offering in pharmaceutical machine learning applications.
North America is poised to maintain the largest share of the market, driven by its strong regulatory infrastructure and early adoption of ML tools. For instance, the March 2024 launch of TrialAssure’s ANONYMIZE 3.0, a machine learning and NLP-based document anonymization platform, addresses EU CTR and CTIS compliance requirements, streamlining global trial transparency and safety. Europe follows, bolstered by regional regulatory directives, while Asia Pacific advances through emerging biotech hubs. Latin America and Middle East/Africa trail but show growing promise due to increased trial outsourcing and digital health investment.
The key features of the market report comprise patent analysis, grants analysis, funding and investment analysis, and strategic initiatives by the leading players. The major companies in the market are as follows:
It is a multinational technology firm headquartered in Armonk, New York, founded in 1911. Its portfolio includes Watsonx AI and legacy Watson Health tools applied in pharmaceutical R&D for predictive analytics, medical data interpretation, and document anonymization. IBM is now concentrating on AI-supported clinical development through Watsonx and data governance solutions.
Headquartered in Mountain View, California and founded in 1998, the company is deploying its deep learning and cloud AI capabilities toward pharmaceutical applications. Its BioNeMo platform, developed by Google’s AI spin-offs and collaborators like SandboxAQ, supports generative modeling of novel small molecules. In June 2025, SandboxAQ, backed by Google and NVIDIA, published 5.2 million synthetic 3D molecular structures to accelerate binding prediction workflows, showcasing Google’s translation of foundational AI into drug discovery innovation.
Based in Redmond, Washington since 1975, it leverages Azure cloud, MLOps, and cognitive services for life sciences AI. The company collaborates with healthcare partners to deploy machine learning in clinical trials, regulatory compliance, and patient engagement. Microsoft’s suite of certified Azure Health services, like secure data environments and AI model governance, supports pharmaceutical ML adoption under regulatory and privacy constraints.
It was founded in 1993 and headquartered in Santa Clara, California. It is a leader in GPU-accelerated computing for life sciences. Its portfolio includes Genomics analysis, AI-enabled drug discovery via BioNeMo, and AI inference platforms. In January 2025, NVIDIA launched partnerships with IQVIA, Illumina, Mayo Clinic, and Arc Institute to develop AI agents for clinical trials and drug discovery acceleration. Additionally, NVIDIA’s collaboration enabled SandboxAQ’s release of 5.2 million synthetic structures, underscoring its industry leadership.
*Please note that this is only a partial list; the complete list of key players is available in the full report. Additionally, the list of key players can be customized to better suit your needs.*
Other players in the market include Deep Genomics, Accenture plc, Cloud Pharmaceuticals, Inc., Atomwise Inc., Intel Corporation, and Oracle Corporation.
"Machine Learning in Pharmaceutical Industry Market Report and Forecast 2026-2035” offers a detailed analysis of the market based on the following segments:
Market Breakup by Offering
Market Breakup by Deployment
Market Breakup by Application
Market Breakup by Region
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*While we strive to always give you current and accurate information, the numbers depicted on the website are indicative and may differ from the actual numbers in the main report. At Expert Market Research, we aim to bring you the latest insights and trends in the market. Using our analyses and forecasts, stakeholders can understand the market dynamics, navigate challenges, and capitalize on opportunities to make data-driven strategic decisions.*
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| REPORT FEATURES | DETAILS |
| Base Year | 2025 |
| Historical Period | 2019-2025 |
| Forecast Period | 2026-2035 |
| Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment:
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| Breakup by Offering |
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| Breakup by Deployment |
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| Breakup by Application |
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| Breakup by Region |
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| Market Dynamics |
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| Supplier Landscape |
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