The world has seen a dramatic change in weather patterns caused by global warming. Extreme weather and unpredictable events are occurring more often. As weather is the most unpredictable component of a flight, if there is a slight chance of predictability in what the weather is going to be, it could help with the logistical issues and prevent further delays. Therefore, aircraft arrival prediction could potentially be a solution to improve the efficiency of the operation and the effectiveness of the contingency plans to respond to such events. The objective of this paper was to gain more insights and study the effect of weather on aircraft arrival delay by using random forest classification method and adaboost classification method. The results show that random forest classifier is the most accurate model for the weather delay prediction with the accuracy of 92.98 percent. When comparing F-1 score with adaboost, random forest model also gives higher values for all conditions.