In order to increase the efficiency of tiny electronic devices, this article looks at the significant convergence of artificial intelligence (AI) and machine learning (ML) with solar photovoltaic (PV) technology. In exploring current developments in wearable and biomedical implants, the study emphasizes the significance of flexible, lightweight, and portable solar cells that may effectively power devices for extended periods of time while using less battery power. The utilization of rechargeable batteries Electronic devices, underscores the significance of integrating AI and ML technologies, which are considered groundbreaking for enhancing the efficacy of solar energy materials. A comprehensive analysis for various ML algorithms demonstrates the rapid and efficient capability to discern materials and designed for solar cells that are both cost-effective and highly efficient. This research introduces an innovative method for classifying literature, incorporating manufacturing techniques, ML algorithms, optimization strategies, and data synthesis. The amalgamation of Bayesian Optimisation (BO) with Gaussian Process Regression (GPR) emerges as a highly promising machine learning approach for devising economical primary objective of this critical assessment is to provide guidance to researchers on leveraging ML methodology to develop affordable solar cells.