Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance
in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine
learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis
development by training the machines in multiple ways. AI-based drug development from molecule identification
to clinical approval tremendously reduces the cost of development and the time over conventional methods.
The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest
example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their
advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic
therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes.
This comprehensive review delves into the fundamental aspects of AI along with its applications in the
realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery
and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations
into drug repurposing and polypharmacology studies.