<p>In the era of modernity and urbanization, the need for manufacturing is booming. Manufacturing data collection is about to reach a pivotal point for major technical developments that might change management and decision-making about manufacturing industrial technology. This research looks at the effective transition of the industrial sector into a modernized one, commonly known as Industry 4.0, that achieved by including Big Data Analytics (BDA) with next-generation technologies. Under this scenario, companies use valuable data from modern sensors and subsequently work with Artificial Intelligence (AI), the Internet of Things (IoT), Machine Learning (ML), Satellite-Based Database-Oriented Cloud Computing (SBDOCC), and their merging with BDA to forecast industry needs, manage supply chains, ensure quantity control, measure demand, and synchronize output with predicted demand by using the Multiple Linear Regression (MLR) method which depends on relative parameters of industry. An innovative calculative program in Python using MLR is proposed here to predict any associated production demands in Industry 4.0 by the concept of huge synthetic data generation from the previous demands of industry that allies the BDA, where the system gets a very low Root Mean Square Error (RMSE) with a very high R-squared (R²) value, which is approximately 1 for test runs consistently. Also, an eminent comparison of other similar ML techniques was performed on the same platform to evaluate the proposed MLR model as the best preference. This study also proposes a distinct framework with a novel Hybrid Architecture (HA) for the manufacturing industry to use BDA alliances with these technologies efficiently. This underlines the importance of building a culture that emphasizes data statistics and makes ongoing investments in both human and technical resources.</p>