This research presents an AI-Based Remote Team Productivity and Project Monitoring System designed to enhance efficiency in distributed work environments. The system integrates machine learning models with real-time analytics to track productivity, analyze performance trends, and predict potential risks such as delays or inefficiencies. The proposed solution utilizes a Flask-based backend and React-based frontend to collect and process user activity data. It generates productivity scores, provides actionable insights through dashboards, and enables managers to make data-driven decisions. Experimental results demonstrate that the system improves monitoring accuracy, reduces manual effort, and enhances overall productivity management. The integration of predictive analytics and visualization tools ensures better workflow optimization and team coordination in remote settings. Keywords: Artificial Intelligence, Productivity Monitoring, Remote Teams, Machine Learning, Data Analytics, Task Tracking, Performance Analysis, Risk Prediction.
Saxena et al. (2026) studied this question.