One of the most difficult and urgent problems in oncology is still predicting how a patient will react to chemotherapy. Interpatient variability still restricts therapeutic success and increases the likelihood of side effects, even with major improvements in treatment regimens. Machine learning (ML) has been a game-changing technique in biomedical research in recent years, allowing high-dimensional information to be integrated and interpreted to inform clinical judgment. With an emphasis on both historical advancements and contemporary advances, this thesis offers a thorough analysis of the function of machine learning in predicting the results of chemotherapy. After examining the fundamental ideas and early applications of machine learning in oncology, we provide a thorough analysis of current supervised and unsupervised learning methods used in chemotherapy response prediction. Neural networks, random forests, support vector machines, and clustering algorithms are important techniques. The use of reputable public datasets as standards for model training and validation, including The Cancer Genome Atlas (TCGA), Genomics of Drug Sensitivity in Cancer (GDSC), and Cancer Cell Line Encyclopedia (CCLE), is also covered in the thesis.Particular focus is placed on real-world clinical application, model interpretability, and performance evaluation criteria. We also discuss data biases, generalizability issues, and ethical problems. Finally, by allowing for therapy customization based on unique genetic and molecular profiles, we investigate how these predictive models can hasten the shift to precision oncology.