This study presents a novel machine learning-based diagnostic model for the timely identification of strokesusingneuroimaging data. Stroke diagnosis is critical, as rapid and accurate detection significantly influences patient outcomes. Theproposedmodel employs a comprehensive methodology utilizing various machine learning algorithms, including Logistic Regression, SupportVector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN), to analyze neuroimagesandpredictstroke occurrences. The model was trained and validated on an extensive dataset of brain images, achieving remarkable per formanceindistinguishing between stroke and non-stroke cases. Notably, the CNNalgorithm demonstrated superior accuracy, achievinganaccuracyof 95.6%, sensitivity of 94.2%, and specificity of 96.5%. The Random Forest and SVM models also performed well, withaccuraciesof93.1% and 92.5%, respectively. This research highlights the potential of machine learning techniques to enhance strokediagnosis,providing healthcare professionals with valuable tools for informed decision-making and ultimately improving pa tient outcomeswhilereducing the economic burden associated with strokesKeywords: stroke identification, machine learning, neuroimaging, convolutional neural networks, diagnostic model, healthcaretechnology