Background: Accurate Postmortem Interval (PMI) determination is critical in forensic investigations. This study addresses the limitations of current PMI estimation methods by investigating the correlation between ambient temperature and cadaver decomposition stages, aiming to develop a predictive model. Objective: The objective is to establish a quantifiable relationship between ambient temperature fluctuations and specific cadaver decomposition stages. The study enhances forensic science by using a comprehensive dataset from simulated forensic scenarios in criminal investigations. Methodology: Data collection involved real-time weather databases and simulated forensic scenarios with staged human cadavers, ensuring diversity and authenticity. The predictive model construction utilized statistical analysis and machine learning algorithms, exploring regression analysis, time-series analysis, decision trees, and neural networks. Results: The predictive model's performance was assessed against simulated and actual PMI data. Comparative analysis showed a close alignment between simulated and predicted PMI values, with accuracy metrics indicating precision. The model exhibited reliability in estimating PMI, suggesting potential applications in real-world forensic scenarios. Conclusion: This study establishes a predictive model for PMI estimation, highlights the relationship between temperature and cadaver decomposition, enhances forensic investigation reliability, and advances methodologies for future forensic science research.