Cancer remains a global health challenge, demanding innovative treatments. Mathematical modeling is a potent tool for understanding, predicting, and optimizing cancer therapies, including virotherapy, chemovirotherapy, and liposomal drug delivery. Virotherapy employs oncolytic viruses to selectively attack cancer cells. Mathematical models focus on viral replication dynamics, tumor-immune interactions, and tumor growth. They aid in timing viral administration, predicting outcomes, and identifying success factors. Models simulate virus-host-immune interplay, guiding safer and more effective virotherapies. Chemovirotherapy integrates chemotherapy with oncolytic viruses, forming synergistic treatments. Models extend principles from both approaches to optimize timing and sequencing of treatments. They consider factors like drug-virus interactions, maximizing efficacy while reducing toxicity. Challenges such as viral resistance and immunomodulation are addressed. Liposomal Drug Delivery targets cancer precisely. Models account for liposomes, drug release kinetics, and transport mechanisms. They predict drug delivery to tumors, optimizing formulations, enhancing bio-availability, and minimizing side effects. Physiological parameters and personalized strategies are also considered. In translating these models to clinical practice, challenges emerge. Incorporating experimental data for model validation is crucial. Collaborations among mathematicians, oncologists, and experimentalists bridge the gap between theoretical models and clinical applications. In conclusion, mathematical modeling advances our understanding of cancer therapies. It offers insights into optimization, personalized treatment, and integrated approaches. By unraveling the complexities of cancer and its treatment, mathematical modeling contributes to the fight against this formidable disease. As therapies evolve, mathematical modeling will remain pivotal in shaping the future of cancer treatment. In forthcoming research endeavors, the exploration of mathematical models on liposomal drug delivery, virotherapy, and chemovirotherapy is anticipated to yield substantial revelations. These insights will play a pivotal role in advancing the realms of big data analytics, deep learning, and machine learning, thereby catalyzing the refinement and customization of cancer treatment approaches, ultimately leading to the creation of highly efficient and tailored therapeutic strategies.