In the era of Industry 4.0, accurate time series prediction is crucial for extracting valuable insights from high-frequency sensor data in Industrial Internet of Things (IIoT) applications. This paper presents EERA: A Hybrid Ensemble Regression Model designed to improve predictive accuracy for time series data in IIoT environments. EERA combines the strengths of multiple base models, including REPTree, SMOreg, and Multi-Layer Perceptron (MLP), through a weighted ensemble approach to achieve better overall performance. The model was tested using a real-world dataset that captures heat index data (temperature and humidity), which has diverse applications in areas such as agriculture, weather forecasting, and enterprise maintenance. Comparative analysis shows that EERA outperforms individual models, achieving a Mean Squared Error (MSE) of 4.150960 & R-squared value of 0.872540, demonstrating high predictive accuracy. These findings suggest that EERA is a dependable &1 effective solution for time series prediction in fast-paced IIoT data environments.