Necrophagous Coleoptera and Diptera are of particular concern in the estimation of the postmortem interval (PMI) because their growth and colonization cycles on putrefying tissue can be forecasted. Despite being well documented, traditional life cycle research is usually plagued by environmental heterogeneity, observer bias, and poor temporal resolution. This chapter presents a new frontier of Artificial intelligence (AI) in the predictive modeling of necrophagous insect growth in the context of altering forensic and ecological environments. We provide an end-to-end description of AI techniques, from supervised machine learning and ANNs to deep learning models for predicting larval growth phase, pupal length, and species succession order. Emphasis was placed on the use of climatic, geographical, and substrate-related variables to develop high-accuracy dynamic models. Through a comparison with empirical data and controlled laboratory data, this chapter demonstrates how AI improves estimation accuracy, minimizes human bias, and maximizes reproducibility. Key challenges include the unavailability of large annotated datasets, biological differences between species, and the need for interpretable models in legal applications. Interdisciplinary integration proposes a blueprint to develop robust, scalable AI systems to aid forensic entomologists in PMI estimation and emphasizes the potential of AI in standardizing and developing forensic science globally.