This paper highlights the growing role of AI technologies in addressing real-world challenges, with a particular focus on optimizing energy harvesting methods, such as piezoelectric and triboelectric nanogenerators. The paper proposes an adaptive approach leveraging machine learning and neural network approaches to progress the collection and optimization of vibration energy. Current progressions in artificial intelligence (AI) and machine learning (ML) have underscored the increasing demand for intelligent, self-sustaining devices. With global concerns over energy consumption, there is an urgent need for solutions that reduce energy usage while ensuring the efficiency of intelligent applications. Energy harvesting, which captures ambient mechanical vibrations to generate electrical energy, offers a viable solution. This paper demonstrates how AI-driven approaches can significantly improve energy harvesting efficiency, contributing to the development of sustainable, intelligent energy solutions. Furthermore, the paper presents results from multiple simulations conducted using MATLAB/Simulink and experimental findings from the dSPACE DS1104 board to validate enhancements in control and speed estimation.