This paper establishes the emergence of AIArtificial Intelligence (AI) technologies tailored to real-world challenges and explores a promising avenue for specific energy harvestingEnergy harvesting methods, notably the optimization of piezoelectricPiezoelectric and triboelectric nanogenerators in terms of recognition, production, and consumption. It proposes an adaptive approach using machine learning and neural network techniques to collect and optimize vibration energy. Recent advancements in computational methodologies, especially within artificial intelligence (AIArtificial Intelligence (AI)) and machine learning (MLMonolayer (ML)), have heightened the need for intelligent, self-sustaining devices. Given the global concern over energy consumption, there is an urgent need for solutions that reduce energy usage while maintaining the efficiency of intelligent applications. Energy harvestingEnergy harvesting technology, which harnesses ambient mechanical vibrations to generate electrical energy, presents a viable solution. This paper aims to demonstrate how cutting-edge AIArtificial Intelligence (AI)-driven approaches can enhance energy harvestingEnergy harvesting efficiency, thereby contributing to the development of sustainable and intelligent energy solutions. Additionally, multiple simulations conducted using MATLAB/Simulink, alongside experimental results obtained with the dSPACE DS1104 board, are discussed to validate the improvements in control and speed estimation.