Artificial Intelligence (AI) and Machine Learning (ML) have emerged as disruptive technologies that are redefining how organisations analyse data and formulate critical business decisions. In the contemporary digital economy, enterprises produce substantial quantities of structured and unstructured data from several sources, including online transactions, social media platforms, consumer interactions, and corporate systems. Conventional analytical techniques frequently prove inadequate for the proper processing and interpretation of extensive datasets. Consequently, predictive data analytics driven by AI and machine learning has emerged as a vital instrument for organisations aiming to get significant insights from data and enhance decision-making processes. Predictive data analytics employs statistical algorithms, machine learning techniques, and data mining approaches to examine historical and real-time data for the purpose of forecasting future trends, behaviours, and results. AI-driven predictive models may uncover concealed patterns and links inside extensive datasets, allowing firms to foresee market fluctuations, comprehend customer inclinations, and manage operational risks with greater efficacy. These competencies enable organisations to transition from reactive decision-making to proactive, data-driven initiatives. This study investigates the influence of AI and machine learning on predictive data analytics to improve business decision-making. The study emphasises the principal methodologies employed in predictive analytics, such as regression analysis, decision trees, neural networks, and deep learning models. It additionally examines diverse business applications including sales forecasting, customer behaviour prediction, fraud detection, supply chain optimisation, and marketing campaign analysis. Organisations may optimise operational efficiency, improve customer satisfaction, and secure a competitive edge in dynamic marketplaces by utilising predictive analytics. The research employs a qualitative methodology, analysing secondary data from academic publications, research articles, and industry reports pertinent to artificial intelligence and predictive analytics. The results demonstrate that AI-driven predictive analytics markedly improves the precision and rapidity of business decision-making by delivering real-time insights and automated analytical functions. Nonetheless, obstacles including data privacy concerns, substantial implementation costs, and the necessity for proficient data professionals persist as significant issues that organisations must confront. AI-driven predictive data analytics constitutes a robust methodology for enhancing strategic planning and optimising organisational performance within a data-centric business landscape.