Intelligent Soil Analytics for Forensic Applications: Emerging Technologies, Trends, and Transformative Paradigms

ABSTRACT Soil is important trace evidence in forensic investigations. Its complex makeup varies by location. Traditional soil analysis methods in forensic science have some issues. They can be subjective. They also lack real‐time capabilities and do not work well with modern analytical tools. This article summarizes recent advancements in smart soil analysis for forensic uses. It highlights new technologies, research trends, and major developments. Key innovations include the application of nanobiosensors for detecting biochemical soil signatures, DNA metagenomics for microbial fingerprinting, and remote or proximal soil sensing for spatial mapping. The use of these technologies with AI and machine learning has led to better classification models. It also supports digital twin architectures that mimic soil behavior in real time. Recent studies show that using soil sensor data with supervised learning algorithms, such as Random Forest and SVM, can reach classification accuracies above 90%. Cloud‐based frameworks improve forensic traceability. They allow for the storage, syncing, and remote analysis of soil evidence. This technological convergence is changing forensic methods. It improves the precision, reproducibility, and legal strength of soil evidence. It also creates connections across agriculture, environmental monitoring, and land‐use planning. The review highlights important challenges. These include data standardization, sensor calibration, and legal admissibility. It also suggests a roadmap for future research. Intelligent soil analytics is key to next‐gen forensic science. It has wide effects on justice, sustainability, and caring for the environment. In addition to its forensic applications, these technologies benefit agriculture and land management, allowing for more informed decisions on soil conservation and nutrient optimization. Real‐time monitoring capabilities enable immediate interventions to address soil health issues, resulting in increased crop yields and reduced environmental impacts. The integration of AI and sensor technology into soil management practices thus holds transformative potential for both forensic science and sustainable agriculture, ensuring long‐term soil health and ecological balance.