Flight planning, as one of the challenging issues in the industrial world, is faced with many uncertain conditions. One such condition is delay occurrence, which stems from various factors and imposes considerable costs on airlines, operators, and travelers. With these considerations in mind, we implemented flight delay prediction through the proposed approaches that were based on machine Learning algorithms. The parameters that enabled effective estimation of delay were identified and then, Bayesian modeling, decision tree, cluster classification, random forest, and hybrid method were applied to estimate the occurrences and magnitude of delay in a network. These methods were tested on a US flight dataset and then, refined for a large Iranian airline network. Results showed that the parameters affecting delay in US networks were visibility, wind, and departure time, whereas those affecting delay in the Iranian airline flights were fleet age and aircraft type. The proposed approaches exhibited an accuracy of more than 70% in calculating delay occurrence and magnitude for both the US and Iranian networks. It is hoped that the techniques put forward in this work will enable airline companies to accurately predict delays, improve flight planning, and prevent delay propagation.