Artificial intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing traditional approaches in chemical and biochemical sciences. This paper explores the significance, benefits, and limitations of AI in the context of drug discovery, emphasizing its role in accelerating the identification of therapeutic candidates and optimizing existing drugs. Leveraging diverse sets of chemical and biochemical data, sourced from reputable databases and literature, the study employs advanced machine learning and deep learning algorithms for predictive modeling. Key AI-driven outcomes include target identification and validation, virtual screening results, molecular docking scores, compound design, optimization, and high-throughput screening automation. The findings showcase superior performance compared to traditional methods, emphasizing the efficiency and accuracy of AI-driven drug discovery. However, challenges such as data quality and ethical considerations underscore the need for ongoing research and development. The paper concludes with insights into collaborative opportunities and areas for further development, highlighting AI's potential impact on personalized medicine and its integration into drug development pipelines. Two key themes, AI and drug discovery, encapsulate the essence of this comprehensive exploration into the current state and future directions of AI in the pharmaceutical domain.