This article discusses the transformative role of artificial intelligence (AI) in drug discovery and development. It highlights how AI, particularly through machine learning (ML) and deep learning (DL), is revolutionizing the process by enhancing efficiency, reducing costs, and accelerating the identification of drug targets, molecular properties, and compound analysis. The traditional drug discovery process is described as lengthy, expensive, and complex, with multiple stages from target identification to clinical trials. AI is increasingly used at various stages of drug discovery to streamline tasks such as cell sorting, compound screening, and toxicity assessment. The article explores the application of AI in drug target identification, molecular dynamics (MD) data analysis, compound screening, drug design and optimization, and toxicity prediction. It also discusses the use of AI in synthetic organic chemistry for planning and optimizing chemical synthesis. The integration of AI with drug discovery is shown to offer a promising strategy to overcome the challenges and complexities of the pharmaceutical industry. The article emphasizes the potential of AI to improve the accuracy and speed of drug development, reduce the failure rate of clinical trials, and enable the discovery of novel therapeutic targets and compounds. Various AI techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning, are discussed as tools for enhancing drug discovery processes. The article also presents case studies demonstrating the successful application of AI in identifying new drugs and inhibitors for diseases such as cancer and Alzheimer's. Overall, the article underscores the significant impact of AI on the pharmaceutical industry, offering a comprehensive review of its applications and future prospects in drug discovery and development.This article discusses the transformative role of artificial intelligence (AI) in drug discovery and development. It highlights how AI, particularly through machine learning (ML) and deep learning (DL), is revolutionizing the process by enhancing efficiency, reducing costs, and accelerating the identification of drug targets, molecular properties, and compound analysis. The traditional drug discovery process is described as lengthy, expensive, and complex, with multiple stages from target identification to clinical trials. AI is increasingly used at various stages of drug discovery to streamline tasks such as cell sorting, compound screening, and toxicity assessment. The article explores the application of AI in drug target identification, molecular dynamics (MD) data analysis, compound screening, drug design and optimization, and toxicity prediction. It also discusses the use of AI in synthetic organic chemistry for planning and optimizing chemical synthesis. The integration of AI with drug discovery is shown to offer a promising strategy to overcome the challenges and complexities of the pharmaceutical industry. The article emphasizes the potential of AI to improve the accuracy and speed of drug development, reduce the failure rate of clinical trials, and enable the discovery of novel therapeutic targets and compounds. Various AI techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning, are discussed as tools for enhancing drug discovery processes. The article also presents case studies demonstrating the successful application of AI in identifying new drugs and inhibitors for diseases such as cancer and Alzheimer's. Overall, the article underscores the significant impact of AI on the pharmaceutical industry, offering a comprehensive review of its applications and future prospects in drug discovery and development.