<p>As a major national strategic scientific challenge, deep geological engineering faces numerous technical hurdles, particularly in the detection and identification of adverse geological formations under complex stratigraphic conditions. With the advancement of next-generation information technologies like big data and artificial intelligence, multi-field collaborative detection technology has emerged as a pivotal direction for deep geological exploration. By integrating real-time multi-field data from drilling and heterogeneous multi-source information, this technology significantly enhances detection accuracy and efficiency, providing critical support for the safe and efficient advancement of deep geological projects. Currently, China still faces challenges in geological anomaly identification, including limited intelligent detection methods and insufficient data fusion capabilities, which constrain the deep implementation of deep geological engineering. Building on domestic and international research progress, this paper systematically analyzes the challenges and development trends in deep geological detection, focusing on three core scientific issues: the mechanism of real-time multi-field collaborative detection in complex geological formations, the development of integrated detection equipment, and multi-source heterogeneous data fusion with 3D tomographic imaging technology. Future trends indicate that multi-field collaborative detection mechanisms are evolving from single-parameter characterization to multi-field coupling and intelligent decision-making, while detection equipment is increasingly moving toward integration and intelligence. Overcoming these key technologies will drive systematic breakthroughs in China’s deep geological exploration, achieving comprehensive progress from methodologies to equipment development.</p>