Neutrosophic and Information Fusion NIF 2836-7863 10.54216/NIF https://www.americaspg.com/journals/show/2410 2023 2023 Department of International Business Management, Tashkent State University of Economics, Uzbekistan Maha Ibrahim This research paper explores the world of nearly real time analytics by focusing on methods of combining information obtained from Environmental Sensor data. The study utilized a customized setup consisting of three arrays of sensors connected to Raspberry Pi devices. It. Analyzed a dataset that encompassed various environmental conditions. By utilizing the Random Forest algorithm this research investigated how sensor readings, including temperature, humidity, LPG concentrations, smoke, light intensity and motion detection can be fused together. The methodology used cross-validation to ensure model training while visually presenting the intricate relationships, between environmental parameters. The results demonstrated the performance of the Random Forest model through visualizations showing Out of Bag (OOB) error rates and a comparative analysis of machine learning classifiers. The findings shed light on the potential of combining information from sensors to enable reliable predictions using Environmental Sensor data. This provides a foundation for advancements in analytics and applications related to environmental monitoring. 2023 2023 08 14 10.54216/NIF.020201 https://www.americaspg.com/articleinfo/39/show/2410