The weather has a significant impact on a wide range of businesses and daily activities, including transportation, agriculture, and disaster relief. Planning for risk mitigation, allocating resources, and making every decision depends on accurate and precise weather pattern forecasts. In recent years, deep learning algorithms have become effective tools in improving the accuracy of weather forecasting. The model uses neural networks to uncover complex patterns from weather data. This study presents the use of deep learning models to predict climate change. This article highlights the shortcomings of traditional methods and the benefits of using deep learning, laying the foundation for our study of how deep learning can improve the accuracy of weather forecasting. Recurrent neural networks (RNN) and Convolutional neural networks (CNN) were used to predict weather patterns. The results indicate that RNN produces accurate results.