Introduction

The estimation of the postmortem interval (PMI), defined as the time elapsed since death, represents one of the most critical tasks in forensic investigations. Accurate determination of PMI can assist in reconstructing the sequence of events surrounding death, narrowing investigative timelines, and corroborating or challenging witness testimonies and alibis. Over the past decades, numerous biological, biochemical, and environmental indicators have been investigated for this purpose. However, despite substantial research efforts, PMI estimation remains a complex challenge due to the multifactorial nature of postmortem processes and the strong influence of environmental conditions on decomposition dynamics [1, 2].

Traditional approaches to PMI estimation are often based on empirical observations or single biological indicators. These methods can be substantially influenced by external factors. Consequently, classical techniques frequently provide relatively broad time intervals rather than precise estimates, particularly in advanced stages of decomposition. In addition, interpretation of postmortem findings often relies on expert judgment, which may introduce inter-observer variability. These limitations have prompted the search for more objective and reproducible analytical methods capable of integrating multiple sources of biological and environmental information to improve the accuracy of PMI estimation [3–5].

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning techniques, has experienced rapid development across biomedical and forensic sciences. These computational approaches are capable of identifying complex and non-linear relationships within large datasets and integrating heterogeneous variables that are difficult to analyze using conventional statistical methods. In forensic medicine, AI applications have progressively expanded to areas such as forensic imaging interpretation, injury pattern analysis, identification of human remains, and predictive modelling of biological parameters relevant to death investigations [6, 7].

More recently, AI-based models have been proposed for the estimation of PMI using a wide variety of data sources, including postmortem imaging, biochemical and metabolomic markers, microbial succession, environmental parameters, and forensic entomology datasets. These approaches aim to enhance predictive accuracy by capturing complex interactions among multiple factors involved in decomposition processes. Early studies have reported promising results [8, 9], suggesting that AI-driven models may outperform traditional approaches, particularly when applied to large multidimensional datasets.

Despite the increasing number of studies investigating AI-based approaches for PMI estimation, the available literature remains highly heterogeneous with respect to study design, data sources, modelling strategies, and validation frameworks. Differences in dataset size, environmental variability, and performance metrics make it challenging to compare findings across studies and to assess the real forensic applicability of these models [7]. Moreover, many studies rely on small experimental datasets or animal models, limiting the generalizability of the results to real forensic casework.

The aim of the present systematic review is to evaluate the current evidence on artificial intelligence–based models developed for postmortem interval estimation. Specifically, this review aims to examine the methodological approaches adopted in AI-driven PMI prediction, the types of data employed, model validation strategies, and the reported predictive performance. By synthesizing the available literature, this work seeks to provide a comprehensive overview of the current state of the field and to identify potential directions for future research and forensic application.