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Safety management in the chemical process industry remains a critical challenge due to recurring high impact industrial accidents and the limited predictive capability of conventional threshold based safety systems. Traditional PLC–SCADA frameworks rely on static alarm limits and reactive shutdown logic, which often fail to detect early stage nonlinear deviations in complex, multivariate processes. This study presents ChemSafeAI+, a machine learning driven dynamic safety and optimization framework designed to augment existing industrial control architectures. The system integrates real-time anomaly detection using gradient-boosting models, predictive analytics, safety action processing, operator aware visualization dashboards, and traceable console logging within a unified, modular architecture. The framework is evaluated using a validated synthetic dataset derived from the Haber–Bosch ammonia synthesis process, capturing realistic thermodynamic, kinetic, and operational variability across 5000 operating scenarios. Experimental results demonstrate strong anomaly detection capability and consistent early warning behavior across multiple abnormal operating conditions. SHAP-based explainability provides both global and local interpretability, aligning model decisions with domain relevant process variables. By combining predictive intelligence with safety oriented decision logic and operator traceability, ChemSafeAI+ demonstrates the feasibility of ML driven supervisory safety systems for proactive risk mitigation and improved operational resilience in industrial chemical environments.