A Review Paper on Machine Learning Models for Weather Forecasting: Techniques, Trends, And Challenges

Monika . Jakhar; Vinod Kumar Srivastava

doi:10.36948/ijfmr.2025.v07i06.63715

A Review Paper on Machine Learning Models for Weather Forecasting: Techniques, Trends, And Challenges

Author(s)	Monika . Jakhar, Dr. Vinod Kumar Srivastava
Country	India
Abstract	Weather forecasting is a crucial point of discussion in agriculture, aviation, disaster management, and energy decision-making. Conventional numerical weather prediction (NWP) models are efficient but need a lot of computer resources and can be inaccurate in the beginning. Machine learning (ML) has become a powerful alternative and addition to traditional forecasting approaches, giving data-driven methodologies that can simulate complex meteorological patterns more efficiently and adaptably. This review covers weather forecasting machine learning methods, including supervised learning models like Support Vector Machines (SVM), Random Forests, and Gradient Boosting and deep learning frameworks like CNNs, RNNs, and LSTM networks. This review focuses on using these models to anticipate temperature, precipitation, wind speed, and humidity across various temporal and spatial scales. Modern model designs, hybrid models, ensemble learning, and satellite and sensor data to improve forecast accuracy are examined. It addresses crucial issues such data quality and accessibility, model interpretability, overfitting, and real-time forecasting. Explainable AI and uncertainty quantification are crucial to trusting machine learning-based weather systems, according to the review. This paper reviews the current and future state of machine learning in meteorological forecasting.
Keywords	Weather Forecasting, Machine Learning, Climate Change, Trends and challenges etc
Field	Computer Applications
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-20
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.63715

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A Review Paper on Machine Learning Models for Weather Forecasting: Techniques, Trends, And Challenges

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