Microplastic (MP) pollution addresses an inevitable and increasing global environmental issue, contaminating biological systems and threatening human health. MPs’ tiny size, varying composition, and extensive dispersal render their detection, quantification, impact assessment, and remediation particularly challenging using traditional methods. These conventional methods are largely time-consuming, labor-intensive, costly, and limited in scope and precision. Artificial Intelligence (AI), such as machine learning (ML), deep learning (DL), computer vision (CV), and advanced data analytics, has greater scope to bridge these limitations. This research explores the booming use of AI technologies in the context of microplastic pollution. AI programs are being used to automate and enhance the sensitivity of MP detection and characterization from intricate environmental matrices (water, soil, air, biota) with the help of imaging (microscopy, spectroscopy) and sensor information. In addition, by means of prescient modeling and design recognition, AI models promote a deeper comprehension of MP environmental fate, transport routes, bioaccumulation flow, and biological risks. In remediation, AI helps refine existing removal technologies (e.g., filtration, degradation processes), develop new mitigation methodologies, and predict the effectiveness of cleanup operations. This paper integrates existing developments, emphasizing the way AI-powered methods can entirely advance the velocity, magnitude, accuracy, and affordability of microplastic surveillance, influence assessment, and correction. While difficulties pertaining to data diversity, model interpretability, and practical application persist, combining AI possesses a gigantic potential for developing more achievable plans to access it and fight the worldwide microplastic epidemic.