The pervasiveness of microplastics in surroundings is required for the identification and measurement as it poses a complex problem. Sampling, separation, detection, and characterization of microplastics are complex and challenging. The conventional methods of analyzing microplastics, like microscopy and spectroscopy, are labor-intensive, time-consuming, and prone to subjective interpretation. Hence, an exhaustive, reliable, accurate, fast, and cost-effective strategy is needed. Recently, artificial intelligence (AI) has emerged as a more viable strategy for improving and streamlining microplastic identification procedures. Due to the rapid AI technology progress in various fields, its use to enhance microplastic identification techniques is examined which covers pipeline analytic steps like data interpretation, spectroscopic analysis, and picture classification. Deep neural networks, computer vision, and machine learning algorithms can be used to automatically identify and characterize microplastics in a variety of samples. We also look at the difficulties and restrictions while using AI in microplastic analysis, including the dataset accessibility, the model’s generalizability, and the algorithm’s transparency. To encourage the development of more reliable and understandable AI solutions for microplastic identification, strategies to solve these issues such as explainable AI, data augmentation, and transfer learning are explained. Finally, we outline potential future paths and developments in the field, stressing the necessity of interdisciplinary cooperation, standardized procedures, and publicly accessible data archives to promote the development and uptake of AI-driven methods in the study of microplastics. Using AI, we can identify microplastics more quickly, learn more about their fate and effects in the environment, and develop evidence-based mitigation plans that protect human health and biodiversity.