The sudden change in environment and behavior and lack of proper and sufficient dataset are still today big primary headache for real-world simulating models. As a result, these simulation-based systems fail to capture the complexity accurately. Also, there are many other issues like noise and fluctuations that often create discrepancies between digital twin’s behavior and actual experience. An important characteristic that every simulating model must have been to dynamically change itself and keep pace with the changing systems which is still today rarely seen in any system. The aim of this research work is to develop a new algorithm named Dynamic Adaptive Simulation Algorithm (DASA) that dynamically change behaviors and parameters based on evolving conditions and widely falls under the computational branch of Adaptive Quantum-Dynamic Probabilistic Modeling (AQIPM). We have compared our DASA algorithm with other advanced models like Gemini, Digital Twin Product Lifecycle Management (DT-PLM), High-Definition Twin Grid (HDT-Grid), SmartSim, Digital Twin Field Area Network (DT-FAN) through simulation-based software Ansys Cloud-Native Generative AI (Artificial Intelligence) for Simulation (Ansys SimAI). We have proved efficiency of our algorithm against all these algorithms by using simulation software. Further, we have also explained it through graphical analysis. For first time we made its simulation trial by utilizing the water demand dataset of one week duration of Pune city through simulating software and the results we concluded along with graphical interpretation is also provided. Besides, such visualizations we clearly corroborate the effectiveness of our approach as well as its catalytic role in predictive modeling.