As decentralized energy systems gain momentum, microgrids (MGs) have become a vital component of the modern power landscape. Yet, maintaining power quality (PQ) within these systems presents ongoing challenges due to the presence of nonlinear loads, variable renewable energy sources, and frequent switching operations. These factors contribute to PQ disturbances, such as harmonic distortion, voltage instability, and synchronization issues. Conventional mitigation methods often struggle to cope with such dynamic and complex environments. This review investigates the emerging role of artificial intelligence (AI) as a powerful tool for optimizing PQ in MGs. It presents a detailed overview of various AI‐based methods, including machine learning (ML), metaheuristics, deep learning, fuzzy logic, and hybrid approaches and their implementation in areas like harmonic suppression, voltage and frequency regulation, islanding detection, renewable energy coordination, and predictive diagnostics. The study evaluates these techniques based on key performance indicators, such as precision, scalability, and suitability for real‐time operation, while also addressing challenges related to data reliability, interpretability, and cybersecurity. The article concludes by highlighting future research directions, such as AI integration with Internet of Things (IoT), edge computing, and decentralized intelligence. Overall, the review illustrates how AI can play a pivotal role in transforming MG PQ optimization for the evolving smart grid era.

The study discusses the design and fabrication of flexible pressure sensors using Ecoflex/Graphene composites. The fabricated sensor is used for the application of intuitive monitoring of human quality gaits and implementation of the soft and comfortable shoe sole for rehabilitation of the patients with foot disorder is also taken into consideration. The sensor is fabricated using molding and casting technique by sandwiching the thin film Ecoflex/Graphene composites between the copper (Cu) electrodes with the dimension of 15 x 15 mm2 with high sensitivity. There are five pressure sensors integrated in the shoe sole, a sensor at the forefoot, three sensors at the midfoot and one sensor at the lower foot (heel). The behavior of the sensor is negative piezoresistive in which the resistance decreases as the pressure increases. The sensors are embedded in a soft and comfortable shoe sole and then integrated with a laptop or mobile application to monitor and analyze human gait in real-time. Furthermore, a dedicated Graphical User Interface (GUI) is designed to read the data. The pressure sensors are integrated with ESP32 microcontroller which wirelessly transmit data to the GUI and smart phones which could be further used in the intuitive monitoring, rehabilitation of the patients with foot disorder or neuromotor diseases.