Drug development is one of the fundamental pillars in the field of medicine and healthcare. Traditional drug screening methods, though useful, are met with innumerable hurdles concerning their finances, efficiency and precision. In order to mitigate these challenges, it was inevitable to capture the intersection of drug development and artificial intelligence. The technological advancements led to the advent of Artificial Intelligence, which has exceptionally transformed the field of drug discovery and screening. Amalgamating AI with drug discovery and development accelerated the proficiency, improving the predictive accuracy of the whole process. It significantly minimised the costs as well as time, developed efficient multi-modal algorithmic models which boosted the performance of AI-assisted computer-aided drug designing. Highly efficient drug screening forms the foundation of recognising prospective compounds from the colossal database. Coalescing AI with drug development involving the evaluation of potential drug candidates has been highly constructive as compared to the conventional in vitro screening methods. The core objective of this chapter is to meticulously delve into the advancing depths of artificial intelligence in the varied steps of the drug screening process via deep learning, machine learning algorithms. It highlights the role of AI in drug discovery, optimisation strategies like pharmacophore modelling, high-throughput virtual screening, as well as quantitative structure-activity relationship modelling. It also explores the challenges encountered while implementing AI like hurdles in interpretability, risk of bias, financial burden, ethical considerations & the strategies to mitigate them are also discussed.