Kashyap, HillolHillolKashyapN C, Vijayakumar2026-04-102026-04-10https://gnanaganga.alliance.edu.in/handle/123456789/10106Flight delays are a significant concern in the aviation industry, affecting operational efficiency, customer satisfaction, and overall airline performance. This dissertation aims to develop a predictive framework using machine learning techniques to forecast departure delay times based on a variety of features, including airport ratings, carrier performance metrics, and real-time weather conditions. The study employs several regression algorithms such as Linear Regression, Decision Tree, Random Forest, XGBoost, Ridge, Lasso, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) to build and evaluate predictive models. Each model is assessed using key performance indicators-R² score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)-to identify the most effective approach. Among all, XGBoost emerged as the most robust and accurate model, offering superior performance in capturing complex, nonlinear interactions among features. To ensure real-world applicability, a Gradio-based web application was developed, enabling users to interact with the model and obtain real-time predictions by inputting relevant flight and weather parameters. Exploratory Data Analysis (EDA), feature importance ranking, and correlation studies were also conducted to enhance model interpretability and domain relevance.enFlight Delay PredictionMachine Learning ModelsXGBoost Algorithm