Gastric cancer (GC) remains a significant clinical challenge due to its frequent late-stage diagnosis and limited treatment stratification. Telomere maintenance genes (TMGs) are crucial in GC progression, but their prognostic value has not been fully explored. This study is the first to integrate TMGs with machine learning to develop a prognostic model for GC. Using clinical and gene expression data from the TCGA database, differentially expressed genes (DEGs) were identified and intersected with TMGs. Prognostic TMGs were determined through Cox regression and machine learning techniques, including Lasso, random forest, and Xgboost algorithms. A five-gene prognostic model (CCT6A, ELOVL4, PC, PLCL1, RPS4Y1) was developed and validated using TCGA data. The model demonstrated strong predictive performance, with AUCs of 0.71, 0.71, and 0.70 at 1-, 3-, and 5-year survival, respectively. High-risk patients had significantly poorer overall survival (OS). Further analysis of the tumor microenvironment (TME) showed that high-risk patients exhibited increased immune cell infiltration, and TMG-associated pathways such as apoptosis, epithelial-mesenchymal transition (EMT), and IL6/JAK/STAT3 signaling were prominent. High EMT scores were linked to worse prognosis. In addition, the hub genes were upregulated in GC patients and cells, correlating with decreased OS. PLCL1 significantly promoted GC cell proliferation, migration, and invasion, and it also activated the inflammation-related pathways in GC. In conclusion, this study not only highlights the prognostic relevance of TMGs in GC but also underscores the clinical translation potential of the prognostic model, offering novel targets for personalized therapeutic strategies in GC.