We all know that the drug discovery process takes years to discover a new drug. Identifying potential drug targets requires years of preclinical research to identify and validate the most promising targets. Artificial intelligence (AI) has emerged as a potent tool for harnessing anthropomorphic knowledge and providing quick solutions to complex problems. Amazing advancements in AI technology and machine learning (ML) present a game-changing opportunity to increase efficiency and accuracy in drug discovery and development. Researchers can identify disease-associated targets and predict their interactions with potential drugs by using AI algorithms that analyse large amounts of biological data, such as genomics and proteomics. This allows for a more efficient and targeted approach to drug discovery from microbes, increasing the likelihood of drug approval success. ML algorithms can predict the bioactivity and toxicity of drug candidates and aid in experimental design. Besides, AI can aid in predicting the mechanism of action, adverse reactions and also play a great role in designing clinical trials and predicting the outcomes. This capability enables lead compound prioritization and optimization, reducing the need for extensive and costly animal testing. AI algorithms can help with personalized medicine approaches, resulting in more effective treatment outcomes and improved patient adherence. This chapter discusses the diverse applications of AI and ML to improve the efficiency of drug discovery, drug screening, designing drug molecules, prediction of the mechanism of action, prediction of the maintenance and quality control, prediction of adverse events, and clinical trial design.