<p>The newest developments in smart energy harvesting technologies are examined in this study, with a particular emphasis on the mutually beneficial link between artificial intelligence (AI) and piezoelectric nanogenerators (PENGs). PENGs are gaining popularity in a variety of applications due to their exceptional capacity to transform mechanical strain into electrical energy, a property of piezoelectric materials. PENGs experience a revolutionary evolution with the integration of AI approaches, augmenting their functionality with features like autonomous control, optimization, and real-time monitoring. Innovative techniques that smoothly integrate PENGs with AI have been made possible by interdisciplinary research encompassing materials science, electrical engineering, and computer science. This allows for adaptive energy harvesting tactics and autonomous operation. This study presents a thorough review of the latest developments in artificial intelligence augmented PENGs, demonstrating how machine learning algorithms maximize electrical outputs in a variety of fields, including as object identification, robotics, medical devices, sound sensors and security systems. Moreover, the application of artificial neural network (ANN)-based approaches for deep learning (DL) and machine learning (ML) improves the security of energy harvesting and makes nanofiber diameter estimation easier, both of which advance production procedures. The potential of AI- driven piezoelectric nanogenerators (PENGs) to improve smart infrastructure and sustainable energy projects is explained in this article. It carefully examines related issues and potential opportunities by delving deeply into the details of system integration, materials engineering, and device production. Additionally, it investigates a number of application sectors, including as self-powered sensors, wearable electronics, and Internet of Things (IoT) devices.</p>