This project aims to enhance tumor detection using ML, specifically Logistic Regression, to classify tumors as benign or malignant with high accuracy. Data from Wisconsin Dataset is preprocessed, including handling missing values and normalizing features. Key predictors are rectified by PCA to improve model performance. The Logistic Regression model is drilled and estimated utilizing criterions like literalness, fidelity, recall, and F1-score. Real-time prediction capabilities support prompt and reliable medical decision-making. By leveraging these advanced analytics, the project seeks to provide clinicians with objective insights, enhance diagnostic precision, support personalized treatment strategies, and improve patient outcomes and healthcare efficiency globally. Moreover, this model’s ease of use ensures it can be applied across various healthcare settings, enabling seamless integration existing medical systems as the model open out through continual improvements it holds the potential to assist in early cancer detection.