Anomaly detection finds patterns that differ from expected norms, it is crucial in a variety of industries, such as cybersecurity, fraud prevention, and health care. This study combines the strengths of the Support Vector Machine (SVM) and Isolation Forest (IF) algorithms to present a strong machine learning framework for anomaly identification. SVM efficiently categorizes and draws lines between normal and anomalous data, whereas Isolation Forest is excellent at separating anomalies by utilizing their unique features. Examined are the theoretical foundations and real-world implementations of the framework, demonstrating the complementing advantages of various approaches. High accuracy and efficiency in identifying abnormalities across a variety of scenarios are demonstrated by experimental assessments on both synthetic and real-world datasets. Comparative evaluations demonstrate the framework’s superiority and scalability over competing methods. This paper advances machine learning-based techniques to anomaly identification by offering practical insights into the architecture, optimization, and use of SVM and Isolation Forest.