In recent times, the presence of microplastic emerged as a serious threat in the environment leading to ecological risk and human health hazard. Contemporary research indicates that microplastic is omnipresent in the environment including terrestrial, aquatic, aerial, and even biological environments, i.e., within organisms as well as the human body. The current project develops a methodology for the automated detection of microplastics based on the Detectron2 framework, where microplastics are the small plastic particles that have a particular concern in the environment due to their widespread occurrence and resistance to degradation. The ability of the library, Detectron2, enables accurate detection and localization of microplastics in a wide range of environments. Firstly the project requires the development of a deep learning model on the data set of microplastic images to identify such small microplastic particles in a variety of factors including lighting, occlusions, and other shapes and colors of the particles. This model outperforms other image processing approaches in terms of precision, recall, accuracy, and mean average precision among other factors. The outcome of the microplastic detection using the Detectron2 model achieved an accuracy of ~92%. This study review methods for detecting microplastic in the environment especially in oceanic water that surrounds the southern coastline of India using Dectectron2 to automate the detection of microplastics in the environment which greatly improves the degree and scale of microplastic monitoring efforts expanding the knowledge on microplastic occurrence and effects of the environment.