The growing development of electronic devices and the need for sustainable energy solutions have stimulated research into alternatives to traditional batteries. In this context, piezoelectric energy harvesters (PEHs) represent a promising technology, capable of converting environmental vibrations into electrical energy to supply low-power devices. However, PEH performance significantly depends on geometry and electrical parameters, and since traditional optimization based on Finite Element (FE) models involves very high computational costs, more efficient strategies are developed. To address this, the thesis proposes a hybrid approach combining machine learning models with genetic algorithms for the optimization of the geometric and electrical parameters of a Quad-Finger multimodal energy harvester.