This project implements a student performance prediction system using a modular machine learning pipeline built in Python. Nine regression models are trained and evaluated - Random Forest, XGBoost, CatBoost, Gradient Boosting, AdaBoost, Decision Tree, K-Nearest Neighbors, Linear Regression, and Extreme Learning Machine (ELM). ELM, introduced as the primary novel addition, is a Single Hidden Layer Feedforward Neural Network that analytically solves output weights via Moore-Penrose pseudoinverse, eliminating iterative backpropagation and achieving competitive accuracy at significantly faster training speeds (Deo et al., IEEE Access, 2020). All models are optimized using GridSearchCV and the best model by R² score is automatically saved for inference. A Flask web application provides a clean, responsive UI for real-time predictions with grade classification.