Educational institutions are increasingly seeking data driven approaches to identify students at risk and enable timely academic interventions. Traditional learning analytics systems embedded into learning managements systems lack predictive capabilities and real time integration, limiting their effectiveness for proactive student support strategies. This study presents a machine learning-integrated learning analytics dashboard that employs XGBoost algorithm for predicting student final grades using real time Canvas LMS data. The system extracts features from assignment group performance categories (homework, quizzes, exams, participation) through automated Canvas API integration. Data preprocessing involves assignment group normalization and temporal filtering based on course completion percentage. The XGBoost model utilizes gradient boosting to learn complex patterns from structured educational data, enabling progressive prediction accuracy improvement from 57.4% at 20% course completion to 82.5% at 50% completion. The dashboard integrates Django based web framework with PostgreSQL database storing Canvas course structures, student enrollments, assignment metadata, and grade records. Real time data synchronization enables dynamic feature extraction by assignment categories. Evaluation on CS2 course data demonstrates superior performance compared to Linear Regression (70.6%) and Random Forest (76.9%) baselines. The integrated system successfully identifies at risk students 4-8 weeks before final grades, enabling targeted academic interventions.