The estimation of the postmortem interval (PMI), or the time elapsed since death, is a fundamental aspect of forensic investigations, as it can provide critical information regarding the time of death. Traditional methods for PMI estimation have been based on physical changes in the body, environmental factors, and the study of insect activity. These methods rely on factors like body temperature, rigor mortis, and livor mortis -postmortem hypostasis or postmortem lividity (Wang et al., Fa Yi Xue Za Zhi 34(5):459–467, 2018). Traditionally, PMI estimation relied on a statistical analysis of errors observed in field studies, calculating backward along a time-dependent curve based on measurable data. However, these methods can be limited by external and environmental conditions and by a lack of comprehensiveness of the literature, in addition to the intrinsic subjective nature of these assessments. All these points constitute a crucial challenge in forensic sciences and investigations (Madea, Roman J Legal Med 20(1):37–42, 2016; Wang et al., Fa Yi Xue Za Zhi 34(5):459–467, 2018). Accurately estimating PMI is critical for several reasons such as establishing a clear timeline of events. PMI helps narrow down the timeframe in which a crime—or a death in general—likely occurred. This can be crucial for many reasons ranging from alibi verification, placing suspects at the scene, or understanding the sequence of events surrounding and leading to a death. Moreover, PMI can be instrumental to identify the cause of death. The rate and patterns of postmortem changes can offer clues about the cause of death. For example, rapid cooling might suggest poisoning, while delayed decomposition could indicate the presence of certain environmental elements. The advent of technology, IT, and recently artificial intelligence (AI) has brought new possibilities in the field of forensic science, offering a more objective and potentially more accurate means of estimating PMI. Such technologies offer promising new approaches in this context. More specifically, this chapter delves into the application of artificial neural networks (ANNs) for PMI estimation in forensic sciences and investigations. We explore the advantages, uses, and challenges of the implementation of ANNs in various PMI estimation scenarios where they have shown promise.