This project presents the design and implementation of an AI-based task management system that automates task classification and priority prediction. The system leverages Natural Language Processing (NLP) and Machine Learning techniques to analyze textual task descriptions, identify relevant categories, and estimate task urgency levels. By reducing reliance on manual classification, the proposed solution improves accuracy, consistency, and efficiency in task management workflows. The system integrates multiple components, including data preprocessing, feature extraction, machine learning models, and a user interface for task monitoring and management. It is designed to support real-world organizational environments where large volumes of tasks are generated across different domains such as software development, testing, documentation, and administrative operations. Experimental results demonstrate that the system achieves high performance in both classification and priority prediction, contributing to better decision-making and improved productivity. This work highlights the practical application of artificial intelligence in automating routine project management tasks and provides a scalable foundation for future enhancements such as real-time analytics, adaptive learning, and integration with collaborative platforms. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy
Omar Rojouleh (Sun,) studied this question.