Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and therapy planning. Computational tools integrating multi-omics, imaging and machine learning have advanced, yet reliable personalized predictions remain challenging. This review analyzes the field through four converging paradigms: classical machine learning, deep learning, graph and network modeling, and mechanistic systems biology. We examine the evolution from correlational features to representation learning, relational inference, and causal simulation of tumor-immune dynamics, highlighting the shift towards multi-modal fusion and interpretable, clinically deployable models. By providing an integrated review of these computational tools, we hope to bring the community closer to achieving precision immuno-oncology for personalized cancer treatments.
© 2026. The Author(s).