Diffuse large B-cell lymphoma (DLBCL) is the most aggressive type of non-Hodgkin’s lymphoma (NHL), and it represents approximately 30% of all NHL cases globally. The clinical and molecular diversity of DLBCL renders treatment problematic, as about 30–40% of patients relapse or become resistant to current regimens like R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). The implications are for precision medicine strategies to match treatments with patients’ unique genetic, proteomic, and clinical characteristics. Emerging developments in bioinformatics and genomics have facilitated the generation of large-scale data, including the Genomics of Drug Sensitivity in Cancer (GDSC) database, a useful tool to investigate drug responses. The complex nature and high dimensionality of these data call for sophisticated analysis methods to unveil actionable information. Herein, ML models were utilized to forecast drug reactions in DLBCL patients by utilizing GDSC data to develop precision oncology. Three machine learning models, logistic regression (LR), random forest (RF), and support vector machine (SVM), were trained and tested using some of the major GDSC features, such as Z-score, LN_IC50 (logarithmic half-maximal inhibitory concentration), and AUC (area under the curve). The outcomes proved that the SVM model, with the radial basis function kernel, had the highest accuracy of 99.88%, performing very well in capturing the intricate, nonlinear relationships of genomic data. Although RF performed well, overfitting was evident. LR, as computationally inexpensive and interpretable as it was, did poorly with the nonlinear patterns in the data, resulting in suboptimal performance. This study highlights the role of ML in revolutionizing oncology by facilitating targeted treatment regimens in DLBCL. By coupling computational models with clinical care, ML-based approaches have the potential to greatly enhance therapeutic response. Future work needs to address dataset bias and model validation in in vivo settings to increase clinical relevance.