The abstract outlines a significant scientific inquiry into the prevalence of micro plastics within the human body and their potential consequences for health. Through the utilization of cutting-edge deep learning methodologies, the study introduces an innovative approach designed to identify and measure micro plastic particles within biological specimens. By harnessing the power of artificial intelligence, the research endeavor’s to enhance our comprehension of how micro plastics interact with the human body and the potential health ramifications therein. The primary objective of this investigation is to illuminate the extent of micro plastic contamination in human tissues and fluids, shedding light on a burgeoning concern in contemporary environmental and public health discourse. Furthermore, the development of an automated detection system represents a pioneering endeavor towards achieving comprehensive analysis of micro plastic exposure and distribution within biological systems. This study holds promise for advancing scientific knowledge regarding the bioaccumulation of micro plastics and their potential physiological impacts. Moreover, the proposed deep learning framework offers a scalable solution for efficiently detecting and quantifying micro plastic presence in diverse biological samples, paving the way for future research endeavor’s and proactive interventions aimed at mitigating the adverse effects of micro plastic pollution on human health.