In the current era of the Digital Industrial Revolution, also known as Industry 4.0 or 4IR, the digital world has access to a vast amount of data, including data from mobile devices, social media platforms, businesses, the Internet of Things (IoT), cyber-security systems, health records, etc. To design and build appropriate cloud-based smart and automated applications and conduct an intelligent predictive analysis of these data to determine the possibilities of future outcomes based on historical data, data patterns, and the useful insights from data to make educated predictions about future events or trends. The key to anticipating such an analysis is having knowledge of machine learning (ML), artificial intelligence (AI), and statistical models. Predictive analysis is carried out using a number of statistical models in cloud computing circumstances. Some of these can be best fitted using state space models, SARIMA (seasonal ARIMA), exponential smoothing models, ARIMA models, and so on. In order to promote data-driven decision-making in the dynamic and changing landscape of cloud technologies throughout this digital industrial revolution, the study examines the advantages, disadvantages, and application of these models. The research provides insight into the effectiveness and applicability of various statistical methodologies, enabling decision-makers to make well-informed decisions for improving workload management, resource allocation, and operational efficiency in cloud environments.