This review delves into recent advancements in machine learning (ML) techniques within the realm of drug discovery, with a particular emphasis on protein kinase inhibitors (PKIs), a critical class of therapeutic compounds. Kinase inhibitors play a pivotal role in treating diseases such as cancer and viral infections. This review not only depicts the latest ML methodologies designed to enhance the prediction, optimization, and discovery of PKIs but also highlights the integral role of bioinformatics tools and databases in these processes. The synergy between ML models and bioinformatics resources has significantly improved the accuracy and efficiency of PKI identification, enabling more robust predictions across diverse biological datasets. By integrating these tools, researchers can streamline the drug discovery pipeline, leading to the development of more effective therapies. This review aims to provide insights into how this interdisciplinary approach can drive the future of drug discovery and the design of novel anticancer agents, offering promising avenues for future research and development.