In the constantly emerging context of cyberspace, the escalating threat of cybercrime demands a nuanced understanding of classification techniques. This work conducts a comparative analysis of Support Vector Machine (SVM), XGBoost, Random Forest, and Naive Bayes for effectively classifying cyber threats. The investigation delves into an in-depth exploration of cybercrime categories—social engineering, identity theft, malware, and espionage—with a specific focus on state-wide cybercrime data in India. The dataset, meticulously sourced from authorized channels, namely Computer Emergency Response Team for International Negotiation (CERT-IN) and National Cybercrime Reporting Portal (NCRP), provides a comprehensive view of cybercrime trends across regions from 2020 to 2023. Rigorous analysis is ensured through the preparation of unstructured data, allowing for a granular examination of the unique traits and societal implications of cybercrimes. The study evaluates the performance of SVM in comparison to XGBoost, Random Forest, and Naive Bayes, offering insights into the strengths and limitations of each algorithm. The findings contribute significantly to the cybersecurity domain, guiding practitioners and researchers in making informed decisions for robust cyber threat identification and mitigation strategies tailored to the diverse cyber landscape in different Indian regions.