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This thesis proposes the enhancing of state estimation and constraint detection of the transmission and distribution grids through the use of machine learning methods. This contribution is relevant once the current methodology suffers from inaccurate or lack of system information in order to perform numerical methods. Taking into consideration the several machine learning techniques presented in the literature, a range of models will be tested in the framing of both classification and regression problems, with the aim of achieving accurate predictions.