<![CDATA[The increasing spread of microplastics throughout the world aquatic ecosystems is a significant ecological and health risk, which highlights an immediate need to develop sophisticated strategies of detection and characterization. The existing analytical approaches to microplastic quantification and identification are commonly not only labor-intensive but also time-consuming and restricted in terms of throughput especially in complicated matrices like soil, river water as well as biosolid fertilizers. Therefore, high-speed, dependable and affordable detection systems are the key to successful environmental surveillance and control measures. To break those limitations, this paper examines the means of integrating artificial intelligence with sophisticated sensor technologies and provides a detailed analysis of the current solutions and suggests new ones to detect microplastic better. In particular, this paper explores the usage of machine learning algorithms to process sensor data, thus making it possible to more efficiently and timely identify, quantify, and even classify microplastic particles. This research paper will seek to give a comprehensive history of some of the sensor modalities, including spectroscopies, optical, and electrochemical techniques, as well as a critical analysis of the AI models, such as deep learning and machine learning, that can be used together to create strong microplastic detection systems. The challenges that this integration tackles include high detection limit, and inability to operate in a portable mode, which is characteristic of the traditional approaches, leading to higher-end, real-time monitoring.]]>
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