Machine learning (ML) is becoming increasingly integral to our lives, revolutionizing industries like healthcare, finance, and manufacturing. Pharmaceutical biotechnology, in particular, stands to benefit significantly from the integration of ML techniques in processes such as drug discovery, personalized medicine, and bioprocess engineering. Traditionally, drug discovery, whether for small molecules, biologics, or vaccines, has been a time-consuming, costly, and risky endeavor. ML algorithms, with their ability to manage vast and complex biological datasets, enable the rapid and precise identification of potential drug candidates. By leveraging large-scale genomic, proteomic, and metabolomics data, ML models can predict molecular interactions, optimize lead compounds, and anticipate drug effectiveness and risk profiles. Additionally, personalized medicine is enhanced through computational methods that identify novel biomarkers and therapeutic targets. Furthermore, ML-driven approaches streamline the drug development pipeline by predicting clinical trial outcomes and identifying patient subgroups most likely to benefit from new treatments, thereby reducing time and costs. In bioprocessing, ML optimizes conditions for microbial and mammalian cell cultures used in biologics production, improving the yield, stability, and purity of biopharmaceuticals. As the pharmaceutical industry grapples with rising costs and lengthy development timelines, the integration of ML offers transformative potential. This chapter summarizes the significant impact of ML across various aspects of pharmaceutical biotechnology.