In the fields of computational biology and healthcare systems, there is a growing use of genomic data from cancer to improve diagnostic accuracy and to formulate patient-specific tumor treatment strategies. Cancer genomics allow researchers to define the genome instabilities and mutations associated with tumor growth, poor diagnosis, and immune cells heterogeneity in the cancer context. This molecular taxonomy of cancer provides a more precise diagnosis to better fight solid tumors and enhance the treatment strategy. Single-cell RNA sequencing techniques in cancer re-search enable the detailed examination of gene expression on a cell-by-cell basis, shedding light on the diversity within the tumor microenvironment. In addition, they can reconstruct evolutionary lineages and detect rare subpopulations. It is important to perform pan-cancer analysis to examine the similarities and dissimilarities in different tumor microenvironments. This study provides a comprehensive comparative analysis of T-cell populations across breast, colorectal, liver, lung, and pancreatic cancers, unveiling T-cell heterogeneity and gene expression patterns specific to these tumors. By analyzing single-cell RNA sequencing data from breast, colorectal, liver, lung, and pancreatic cancers, our research fills a crucial gap in understanding the diverse immune environments of these malignancies. The study highlights similarities and differences in T-cell populations across these cancers, providing insights for cancer immunotherapy strategies. Our insights into gene expression correlations with tumor progression have significant implications for immunotherapy strategies, suggesting potential biomarkers for personalized treatment approaches and highlighting key molecular targets for future therapeutic development.