AI in Omics Studies Market Size to Surpass USD 6.11 Billion by 2035

What is the AI in Omics Studies Market Size in 2026?

The global AI in omics studies market size accounted for USD 1.25 billion in 2025 and is predicted to increase from USD 1.47 billion in 2026 to approximately USD 6.11 billion by 2035, expanding at a CAGR of 17.20% from 2026 to 2035. The market is rapidly growing due to the increasing demand for precision medicine, accelerated drug development to combat rare disease burden, rapid AI adoption by the healthcare industry, and advancements in AI technologies.

Key Takeaways

• North America held the largest market share of 42% in 2025.
• The Asia Pacific is expected to grow at the fastest CAGR of 19.5% between 2026 and 2035.
• By omics type, the genomics segment dominated the market with a 40% share in 2025.
• By omics type, the proteomics segment is the second-largest shareholder and is expected to grow at a significant CAGR of 17.5% between 2026 and 2035.
• By component, the software segment dominated the market with a 55% share in 2025.
• By component, the services segment is the second-largest shareholder and is expected to grow at a significant CAGR of 16.5% between 2026 and 2035.
• By application, the drug discovery segment dominated the market with a 35% share in 2025.
• By application, the precision medicine segment is the second-largest shareholder and is expected to grow at the fastest CAGR of 18.5% between 2026 and 2035.
• By end use, the pharmaceutical & biotechnology companies segment dominated the market with a 50% share in 2025.
• By end use, the research institute segment is the second-largest shareholder and is expected to grow at a significant CAGR of 17.5% between 2026 and 2035.

Market Overview

AI in omics studies focuses on using advanced technologies to analyze large and complex biological data, such as genomics, transcriptomics, proteomics, and metabolomics. These datasets help researchers understand how genes, proteins, and other biological factors interact in the human body. The goal is to identify important patterns that can support faster biomarker discovery and enable personalized medicine.

Artificial Intelligence, especially Machine Learning (ML) and Deep Learning (DL), plays a key role in this process. ML is widely used to manage and analyze diverse and complex datasets, while DL is more effective in areas like drug discovery, disease classification, and understanding biological mechanisms. By combining multiple layers of biological data—such as DNA mutations, RNA expression, and protein activity AI provides a more complete view of diseases. This helps researchers develop more targeted treatments and improve drug development.

The AI in omics studies market is growing rapidly due to several factors. The increasing availability of big data in the biotechnology sector, rising demand for personalized healthcare, and the growing use of AI in advanced fields like spatial biology are major drivers. There is also a shift from traditional data analysis methods to more predictive and advanced modeling approaches. This change is helping researchers gain deeper insights and develop more accurate, data-driven solutions for disease treatment and drug discovery.

AI in Omics Studies Market Trends

One of the key trends in this market is the development of AI systems that can analyze multiple layers of disease-related data and suggest approved drugs that target those specific conditions. This approach helps speed up the treatment process, especially for rare and complex diseases.

Another important trend is the improvement in how AI models are trained. Instead of focusing only on statistical patterns, these models are now designed to identify biologically meaningful connections. This ensures that the results are not just accurate but also relevant from a scientific perspective.

There is also a growing shift in the biotech industry from “black box” AI systems, where results are hard to interpret, to more transparent “glass box” models. Researchers are increasingly using Explainable AI (XAI) to clearly show how AI arrives at its conclusions. This improves trust, supports clinical decision-making, and helps meet regulatory requirements.

Market Scope