Big Data Analytics and Predictive Modeling for Climate Change Impact Assessment

The onset of the Fourth Industrial Revolution has brought rapid technological changes but has also hastened world problems such as climate change, urbanization, and population increase. As technologies like sensors, data mining, and Big Data Analytics have matured, research on climate change has departed from traditional random sampling to data-rich, in depth analysis. At the same time, Artificial Intelligence (AI) and machine learning have proven to be great tools for building sophisticated predictive models to evaluate the complex effects of climate change. Combining Big Data and AI methods with climate science allows complex data sets to be gathered, analyzed, and interpreted, and therefore better forecasts of future climate conditions, adaptation plans, and past impact estimates. Despite all these advances, challenges like data gaps, quality issues, and ethical issues in converting AI insights into actionable policy suggestions remain. Overcoming them needs hybrid approaches that integrate AI with physical models, the creation of synthetic climate datasets, and the establishment of AI-driven decision support systems for policymakers. This paper gives a complete picture of the existing scenario, opportunities, and challenges in utilizing Big Data Analytics and Predictive Modeling for assessing the impacts of climate change, underlining their vital role in propelling sustainable, resilient solutions for the future.