<p>Artificial Intelligence has enriched drug Discovery and development, making it faster and easier. This Review article summarizes the use of AI across various stages of drug discovery and development, as well as in marketing and commercialization. It also covers currently used software for predicting protein structure and confirmation, for example, sites like Transcripta Bio, FlexPose, SBAlign, NeuralPlexer, DiffDock-Pocket, and AlphaFold, which provide research and briefs on how AI is influencing current drug discovery and development, making it rapid and precise. The review gives information about various software used in screening of lead molecules and target identification, with a practical example that makes researchers more interested to deep dive into reading this article and understand today's changing world in the field of drug discovery and development. The article also focuses on the use of AI in pre-formulation, predicting pharmacokinetic and pharmacodynamic, analytical method development, preclinical and clinical studies. Highlights on the use of AI in regulatory filing, marketing, and commercialization, it also covers the ease of handling a large amount of data through AI drawn from various pharmacovigilance websites and centers. The article deals with the use of AI in the entire drug discovery process, till its commercialization and post-marketing surveillance. This review examines the transition in drug discovery from traditional, human-intensive experimental methods to software-based approaches utilizing artificial intelligence. These advancements enable the rapid screening and optimization of large numbers of drug candidates. For instance, Transcripta Bio employs data from 200 million molecules to screen genes using I<i>n-silico</i> tools and AI models. Use of Troxerutin as an anti-viral drug against SARS-CoV-2 was due to its finding as a Transient receptor potential vanilloid 1 (TRPV1) antagonist and which was found using a deep learning AI model and machine learning.</p>