Worldwide, flight delays are becoming a major issue for the airline business. Due to air traffic congestion brought on by the airline industry's expansion over the last 20 years, flights have been delayed. In addition to hurting the economy, flight delays also have a detrimental effect on the environment since they increase fuel consumption and gas emissions. Thus, it is essential to take all reasonable precautions to avoid flight delays and cancellations. This paper's primary goal is to forecast an airline's delay utilizing a variety of variables. Thus, it is necessary to perform forward-looking analysis, which encompasses a variety of algorithmic predictive analytics approaches that use historical and current data to create models that are used for forecasts or simply to look at future delays using machine learning algorithms like Python 3's Gradient Boosting Regression technique, Bayesian Ridge, Random Forest Regression, and Logistic Regression. This will make it easier for the user to forecast whether an aircraft will arrive on time or not. Additionally, delay prediction analysis will assist airline industries in reducing their losses.