Single-cell technologies generate vast and complex datasets that require advanced computational methods to extract meaningful insights. Our research leverages machine learning to uncover hidden patterns in single-cell RNA sequencing (scRNA-seq), TCR sequencing, and multi-omics data. We develop and apply predictive models to infer cellular states, track dynamic transitions, and predict functional properties such as T cell clonality and therapy response. By integrating deep learning, probabilistic modeling, and constraint-based approaches, we aim to enhance our understanding of cellular heterogeneity and improve the interpretation of single-cell data in cancer research. These tools provide a foundation for novel biomarker discovery and precision medicine applications.