<p>The predictive capability of machine learning plays a crucial role in aiding forensic practitioners in decision-making regarding opinions. However, the intricate specialization and complexity involved in developing machine learning models impede their comprehensive utilization within forensic science research and practical identification. The utilisation of Advanced Data Analysis (ADA) tools based on the ChatGPT-4 provides strategies to address this challenge by simplifying the machine learning process. The objective of this study was to assess the efficacy of autonomously machine learning models for ADA in diverse tasks by providing ADA with an array of data types, with postmortem interval (PMI), injury time, and sudden cardiac death (SCD) serving as illustrative examples. ChatGPT ADA is capable of autonomously conducting data standardization and selecting the optimal machine learning model based on the raw data. A comparison of the prediction results of ADA with those generated by machine learning models developed by professional data analysts revealed that ADA demonstrated robust predictive performance across diverse datasets. Furthermore, no statistically significant differences were observed in the evaluation metrics across the models when compared to those constructed by data analysts. In conclusion, for the forensic field with a greater number of applications, ChatGPT ADA simplifies the intricate construction process of machine learning and offers a prospective instrument for the comprehensive implementation of machine learning in forensic research and practice by emulating human discourse. However, ADA should not supplant researchers but rather serve as a supplementary tool for research, avoiding its misuse as an “all in” predatory analysis instrument.</p>