This research introduces the Constrained Multi-Objective Osprey Optimization Algorithm (CMO-OOA), a novel extension of the Osprey Optimization Algorithm (OOA). The extension is designed for constrained optimization problems having multiple objective functions. The method is inspired by the evolutionary tendency of an osprey to search and hunt prey from the water. The intelligent natural behaviour of hunting and transporting its prey to an optimal position for consumption serves as the foundation for the design and development of the method. The proposed method overcomes the drawback of the traditional OOA by incorporating multiple objective functions and constraints. This approach uniquely integrates the sorting approach of Pareto dominance-based fast non-dominated sorting and crowding distance approach. This combination enables the OOA to sort multiple objectives while preserving diversity among solutions. Further, the feasibility of solutions is dealt with a constraint violation approach and solutions after each run are stored in an external archive. As a collective, the aim is to identify the Pareto optimal front as efficiently as possible. CMO-OOA is rigorously evaluated with a comprehensive set of multi-objective benchmarks including Bnh, Constr-Ex, CnH, Test and, Osy and a few engineering design problems like front rail, welded beam, and multiple disk clutch brake. Furthermore, the optimization results are compared with a few well-known optimizers including NSGA-II, C-MOEA/D, MFO-SPEA2, ICMA and CCMO, to comprehensively evaluate the method’s robustness and effectiveness. Quantitative analysis uses the GD, HV, CVG metrics and runtime to assess convergence and distribution, and qualitative aspects are illustrated through graphical representations.