Finding a balance between conflicting interests in multipurpose reservoirs is an important challenge for decision makers. This study assesses the use of different computational tools to obtain optimal reservoir operations at the Hatillo dam in the Dominican Republic. A multi-objective optimization approach is applied to models that simulate reservoir operations and three different machine learning (ML) models are employed to learn the real operation of the system. A general model is proposed to simulate daily reservoir operations (2009–2019), integrating water balances, physical constraints of the dam components, and the ML models, the latter defining daily controlled discharges. In the optimization process, the ML parameters are the decision variables, while the objectives evaluated are irrigation, hydropower generation, and flood control. The results are compared with the actual operation of the reservoir. The flood control objective was found to have a wide room for improvement over the real operation of the reservoir, and several of the solutions were found to improve the real operation for the three proposed objectives. The multilayer perceptron models tended to generate the best results for this case study and the nondominated sorting generic algorithm (NSGA II) optimizer generated the best optimization results.