2026, Vol. 7, Issue 1, Part B

Big data analytics plays a pivotal role in the development of artificial intelligence (AI), particularly in the realm of predictive Modelling. As industries generate vast amounts of data, leveraging this information for accurate predictions has become central to decision-making processes across sectors such as healthcare, finance, marketing, and manufacturing. Predictive Modelling involves using historical data to forecast future outcomes, and the integration of AI methods, such as machine learning and deep learning, has significantly enhanced the accuracy of these predictions. Big data analytics offers the ability to process and analyze vast, complex datasets, enabling the extraction of meaningful patterns and trends that were previously difficult to discern.

The growth of big data has been propelled by advances in technology, including the proliferation of sensors, social media, and internet-connected devices. AI tools, particularly those utilizing machine learning algorithms, have the ability to process this data in real-time, facilitating faster and more precise predictions. However, this progress is not without challenges. Issues such as data privacy, computational resource requirements, and the need for effective data cleaning techniques remain critical obstacles to the full potential of big data analytics in AI. Moreover, the evolving nature of data necessitates continuous adaptation of AI algorithms to ensure their relevance and accuracy.

This paper aims to explore the integration of big data analytics in AI, focusing on its applications in predictive Modelling. It addresses the potential benefits and challenges, offering a comprehensive view of how AI techniques, coupled with big data, can enhance predictive capabilities in various domains. The research aims to demonstrate the importance of robust data analysis frameworks for optimizing AI performance, while also exploring the ongoing research in overcoming current limitations.