Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181104,
        author = {Prakhar Rastogi and Dr. Pawan Saxena},
        title = {Machine Learning Applications in Climate Science:Novel Approaches to Prediction and Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3582-3592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181104},
        abstract = {Climate Concurrent climate change issues require new strategies in environmental prediction and live monitoring. Conventional General Circulation Models (GCMs) and Numerical Weather Prediction NWP) models provide basic insights but are hampered by computing constraints and have difficulties with the nonlinear nature of environmental information.This study assesses how climate forecasting abilities for temperature, precipitation, and humidity readings can be remapped by intricate machine learning structures in the guise of Long Short-Term Memory (LSTM) networks, Random Forest techniques, and Support Vector Machines (SVM). These ML methodologies show improved accuracy, flexibility, and scalability through the integration of heterogeneous sources of global climate data sets, IoT sensor networks, and satellite imagery. The study addresses new challenges by using explainable AI techniques, federated learning approaches, and hybrid learning and aims at real-world applications in city planning, farm optimization, and catastrophe avoidance.physical-statistical approaches. The article concludes with policy recommendations for merging machine learning technologies with broader climate resilience measures.},
        keywords = {reinforcement learning, game AI, deep Q-networks, policy gradient, actor-critic, intelligent agents},
        month = {June},
        }