Osteosarcoma is generally considered a cold tumor and is characterized by epigenetic alterations. Although tumor cells are surrounded by many immune cells such as macrophages, T cells may be suppressed, be inactivated, or not be presented due to various mechanisms, which usually results in poor prognosis and insensitivity to immunotherapy. Immunotherapy is considered a promising anti-cancer therapy in osteosarcoma but requires more research, but osteosarcoma does not currently respond well to this therapy. The cancer immunity cycle (CIC) is essential for anti-tumor immunity, and is epigenetically regulated. Therefore, it is possible to modulate the immune microenvironment of osteosarcoma by targeting epigenetic factors. In this study, we explored the correlation between epigenetic modulation and CIC in osteosarcoma through bioinformatic methods. Based on the RNA data from TARGET and GSE21257 cohorts, we identified epigenetic related subtypes by NMF clustering and constructed a clinical prognostic model by the LASSO algorithm. ESTIMATE, Cibersort, and xCell algorithms were applied to analyze the tumor microenvironment. Based on eight epigenetic biomarkers (SFMBT2, SP140, CBX5, HMGN2, SMARCA4, PSIP1, ACTR6, and CHD2), two subtypes were identified, and they are mainly distinguished by immune response and cell cycle regulation. After excluding ACTR6 by LASSO regression, the prognostic model was established, and it exhibited good predictive efficacy. The risk score showed a strong correlation with the tumor microenvironment, drug sensitivity, and many immune checkpoints. In summary, our study sheds new light on the CIC-related epigenetic modulation mechanism of osteosarcoma and helps search for potential drugs for osteosarcoma treatment.