10.46243/jst.2025.v10.i02.pp51-65 registered

The understanding of complex atmospheric phenomena to forecast wildfires with high accuracy has beendramatically transformed with the introduction of cognitive artificial Intelligence. Incorporating state of theart machine learning tools, including deep learning, Bayesian analysis along with decision trees, neuralnetworks, nearly all information from satellites and data collected in the past has been put into thesesystems. These systems permit cognitive AI to provide unparalleled forecasting that is granular and temporalaccurate thanks to its pattern recognition and real time variable adjustments capabilities.Research case studies covering the 2018 Camp Fire, the Bobcat Fire in 2020, and the Dixie Fire of 2021,have all supported AI's ability to predict and prevent the loss of property and lives. Many crucial strategiesincluding evacuation planning, resource deployment, and long-term wildfire prevention strategies inSouthern California have improved cognitive AI implementation. However, the remaining challenges are dataquality and availability issues, integration with existing management systems, and ethical considerationssurrounding of AI decision-making. This research focuses on fire behaviour simulations, increasing datafusion techniques, and. adaptive learning models. Integration of cognitive AI model with evolving technologieslike drones, IoT sensors, and edge computing holds a magnificent potentials for creating a more efficient andresponsive wildfire management ecosystem. Unsurprisingly, problems with AI technology remain, such asthe need for system integration, data inconsistencies, and ethical issues that AI decisions bring. AI decisionspose a mix of issues that are deeply analytical and calculative.