Shagufta Areej, Dr. I. Samuel Peter James 02 08 Stroke is one of the most prevalent causes of death and disability in the world although it is preventable and treated Improving clinical outcomes and lowering the burden of disease are significantly aided by early stroke detection and prompt treatments Because machine learning techniques can be used to detect strokes they have garnered a lot of attention in recent years Finding trustworthy techniques algorithms and characteristics that support healthcare providers in making wellinformed decisions on stroke prevention and treatment is the goal of this project In order to accomplish this we have created an early stroke detection system that uses CT scans of the brain and a ResNet Residual Network model to identify strokes at an extremely early stage The ResNet model is used for image classification in order to obtain the most pertinent features for categorization The systems efficacy was assessed using crossvalidation using metrics including precision recall F1 score ROC Receiver Operating Characteristic Curve and AUC Area Under the Curve The suggested diagnostic system enables doctors to treat stroke patients with knowledge Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026