This research includes complex artificial intelligence algorithms specifically designed for real-time fraud detection. It combines advanced segmentation methods, biometrics for behavior, and geographic analysis with interaction features from user-generated datasets, like keyboard dynamics and behavioral metrics. Learn more about random forest, logistic regression, support vector machine (SVM), XGBoost, and CatBoost, among other machine learning methods, in order to get a better sense of how to recognize anomalies and identify multiple types of users. With an exceptional ROC-AUC score and an ultimate accuracy of 96%, CatBoost shows how well the model detects fraudulent behavior. A key component of fraud protection measures is the use of geographic analysis, which enhances the capacity to spot anomalies based on enigmatic spatial patterns. This method accurately finds and measures fraud in cybersecurity by combining machine learning, behavioral analysis, and geographic intelligence. This creates solutions that can be used by many people and are reliable. We use SMOTE and isolation forest approaches to systematically handle significant issues like feature selection and class imbalance. Spatial analysis is a crucial component of fraud prevention measures because it makes it easier to identify anomalies based on invisible spatial trends.