Many-Objective Optimization Problems (MaOPs) are a class of complex optimization problems deined by having more than three objective functions. Traditional Multi-Objective Evolutionary Algorithms (MOEAs) have shown poor scalability in solving this kind of problem. The use of machine learning techniques to enhance optimization algorithms applied to MaOPs has been drawing attention due to their capacity to add domain knowledge during the search process. One method of this kind is inverse modeling, which uses machine learning models to enhance MOEAs diferently, mapping the objective function values to the decision variables. This method has shown a good performance in diverse optimization problems due to the ability to directly predict solutions closed to the Pareto-optimal front, among these methods, we can highlight the Decision Variable Learning (DVL). The strategies involving inverse models found, including the DVL, have some limitations such as the exploration of the performance of diferent machine learning models and the strategies in using the generated knowledge during the search. The main goal of this work is to create a framework that uses an inverse modeling approach coupled to any MOEA found in the literature. More precisely, three main steps were taken to achieve the goals. First, we perform a systematic review of the literature to identify the main uses of machine learning techniques enhancing optimization algorithms. Secondly, we analyze the performance of diferent machine learning methods in the DVL, seeking to understand the main characteristics of inverse modeling through the DVL algorithm. In the last step, we propose a framework that is an extension of the DVL algorithm, based on the knowledge obtained in the systematic review and our analysis of the DVL. This framework results in an algorithm for MaOPs recommended for situations that exist restrictions on the number of evaluations in the objective function.São Cristóvão