Ovarian cancer (OV) is highly heterogeneous tumor with a very poor prognosis. Studies increasingly show that T cell exhaustion is prognostically relevant in OV. The aim of this study was to dissect the heterogeneity of T cell subclusters in OV through single cell transcriptomic analysis. The single RNA-sequencing (scRNA-seq) data of five OV patients were analyzed, and six major cell clusters were identified after threshold screening. Further clustering of T cell-associated clusters revealed four subtypes. Pathways related to oxidative phosphorylation, G2M checkpoint, JAK-STAT and MAPK signaling were significantly activated, while the p53 pathway was inhibited in the CD8+ exhausted T cells. The standard marker genes of CD8+ T cell exhaustion were screened to develop a T-cell related gene score (TRS) based on random forest plots in TCGA cohort. The patients with low TRS have better prognosis compared to the patients with high TRS in both TCGA and GEO. In addition, most genes included in the TRS showed significant differences in expression levels between the high- and low-risk groups. Immune cell infiltration was analyzed using the MCPcounter and xCell algorithms, which revealed significant differences between the two risk groups, indicating that the different prognoses may stem from the respective immune landscapes. In addition, CD38 knockdown in OV cell lines increased apoptosis and inhibited invasionin vitro. Finally, we performed a drug sensitivity analysis and identified six potential drug candidates for OV. To summarize, we identified the heterogeneity and clinical significance of T cell exhaustion in OV and built a superior prognostic model based on T cell exhaustion genes, which can contribute to the development of more precise and effective therapies.