This study explores the advancement of digital twin technology, emphasizing its progress and addressing existing limitations. A univariant temperature-driven component twin approach is proposed to streamline digital twin development by focusing on a single critical variable for modeling and monitoring. The approach was validated through a case study involving the construction of a digital twin to assess the deterioration characteristics of Rocket case insulation Rubber (ROCASIN). Experimental analyses were conducted under varying temperature conditions to evaluate its mechanical properties and degradation behavior. A virtual model was developed using a machine learning and deep learning-based modeling strategy, with a Random Forest algorithm as the primary method based on the comparative analysis with Linear Regression, Polynomial Regression, Decision Tree, Long Short-Term Memory, Artificial Neural Networks, XGBOOST, and Support Vector Machine. The Random Forest model achieved high accuracy (MAE: 0.0258, MSE: 0.0049, R2: 0.999), and the virtual model was integrated into the physical entity to develop a digital twin framework, enabling real-time monitoring and predictive maintenance (error: 0.0013–0.0088). This framework enhances durability and performance while simplifying digital twin implementation.