Our work explores the possibility of creating a digital twin (DT) platform for cancer to better understand the progression of an individual's cancer. By simulating the unique characteristics of each tumor and its response to treatments, we aim to offer insights into personalized cancer therapy. Our method combines elements of mechanistic modeling, machine learning, and stochastic techniques to develop a DT platform. This platform makes use of diverse data types, such as biological information, biomedical data, and electronic health records (EHR), to create individualized predictions. A central aspect of our approach is the use of a mechanistic model based on quantitative systems pharmacology (QSP). QSP is a computational method used to analyze drug interactions and effects, and it plays a crucial role in our project. The model includes a large system of nonlinear equations modeling both bio-chemical and bio-mechanical integrations in the tumors. We acknowledge that a common challenge in QSP modeling is accurately determining parameters, especially since traditional models often assume a general uniformity across different patients' diseases. This assumption can lead to limitations when calibrating parameters using varied data sources.