AI-Based Maritime Piracy Detection and Risk Zone Prediction System

This research presents an Artificial Intelligence-based framework for maritime piracy detection and risk zone prediction using machine learning techniques. The study focuses on analyzing historical piracy incident data to identify hidden patterns, detect high-risk maritime regions, and support predictive decision-making for maritime security operations. The proposed system integrates structured data management with machine learning pipelines to process and analyze maritime datasets efficiently. Multiple algorithms, including classification and clustering techniques, are applied to extract meaningful insights from the data. The system is designed to operate in a secure offline environment, ensuring data confidentiality and suitability for defense and restricted operational settings. In addition, the framework incorporates data visualization techniques to transform complex analytical outputs into interpretable graphical representations, enabling improved situational awareness and decision support. The system demonstrates the effectiveness of Artificial Intelligence in enhancing maritime surveillance, threat detection, and operational planning. This work highlights the potential of machine learning-based systems in improving maritime security and provides a scalable approach that can be extended to other domains such as border surveillance, disaster management, and critical infrastructure protection.