Anoikis, a form of programmed cell death linked to cancer, has garnered significant research attention. Esophageal cancer (ESCA) ranks among the most prevalent malignant tumors and represents a major global health concern. To ascertain whether anoikis-related genes (ARGs) can accurately predict ESCA prognosis, we evaluated the predictive value and molecular mechanisms of ARGs in ESCA and constructed an optimal model for prognostic prediction. Using the Cancer Genome Atlas (TCGA)-ESCA database, we identified ARGs with differences in ESCA. ARG signatures were generated using Cox regression. A predictive nomogram model was developed to forecast ARG signatures and patient outcomes in ESCA. Gene set enrichment analysis (GSEA) was employed to uncover potential biological pathways associated with ARG signatures. Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) and cell-type identification by estimating relative subsets of RNA transcripts analyses were used to assess differences in the immune microenvironment of the ARG signature model. Based on ARGs, the patients with ESCA were divided into high and low groups, and the sensitivity of patients to drugs in the database of genomics of drug sensitivity in cancer was analyzed. Finally, the correlation between drug sensitivity and risk score was then evaluated based on the ARG signatures. Prognostic relevance was significantly linked to the ARG profiles of 5 genes: MYB binding protein 1a (MYBBP1A), plasminogen activator, urokinase (PLAU), budding uninhibited by benzimidazoles 3, HOX transcript antisense RNA, and euchromatic histone-lysine methyltransferase 2 (EHMT2). Using the risk score as an independent prognostic factor combined with clinicopathological features, the nomogram accurately predicted the overall survival (OS) of individual patients with ESCA. Gene ontology (GO) enrichment analysis indicated that the primary molecular roles included histone methyltransferase function, binding to C2H2 zinc finger domains, and histone-lysine N-methyltransferase activity. GSEA revealed that the high-risk cohort was connected to cytokine-cytokine receptor interaction, graft-versus-host disease, and hematopoietic cell lineage, whereas the low-risk cohort was related to arachidonic acid metabolism, drug metabolism via cytochrome P450 and fatty acid metabolism. Drug sensitivity tests showed that 16 drugs were positively correlated, and 3 drugs were negatively correlated with ARG characteristic scores. Our study developed 5 ARG signatures as biomarkers for patients with ESCA, providing an important reference for the individualized treatment of this disease.