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This study presents a pioneering investigation into the complex Holocene paleo-morphologies of the Yilan Plain, located at the southwestern edge of the Okinawa Trough. We employed a novel approach that synergized resistivity measurements with machine learning techniques to unlock valuable insights into the geological history, sedimentary patterns, and seismic activity of this dynamic region. Our methodology involved the creation of an Apparent Geological Model (AGM) through the interpolation of inverted resistivity data and the application of supervised machine learning algorithms. Classification criteria, derived from the relationship between resistivity values and sediment types found in nearby boreholes, were developed using the random forest machine-learning method. The resultant 3D resistivity model was transformed into a clay-sand-gravel model, offering a comprehensive depiction of sediment distribution within the Yilan Plain. Notably, our findings revealed distinct sedimentary patterns. Gravel-dominated regions, characterized by resistivity values above 140 Ohm-m, were identified alongside areas dominated by sand and clay sediments. The Carbon-14 dating ages in the sand sediments exhibited remarkable consistency, shedding light on the depositional history of the region. Furthermore, our research unveiled a previously unknown phenomenon of rapid subsidence in the Yilan Plain. Through meticulous analysis and correction for sea-level changes, we estimated an average subsidence rate of approximately 8.5 mm/year. This subsidence was punctuated by abrupt events around 6000–7000 years BP and 2000–3000 years BP, associated with a sudden increase. These events suggested a potential link to prehistoric seismic activity, with variable subsidence rates between episodes hinting at recurrent active seismic periods every 4000–5000 years. In conclusion, our multidisciplinary approach has provided unprecedented insights into the Holocene paleo-morphologies of the Yilan Plain. By combining resistivity measurements, machine learning, and geological analysis, we have enriched our understanding of the region's geological history, sedimentary dynamics, and seismic behavior. These findings not only contribute to the knowledge of Yilan's past but also offer vital data for future environmental and geological studies in similarly dynamic regions. Key Points: The study investigates Holocene paleo-morphologies in Taiwan's Yilan Plain, providing insights into its geological history. By combining resistivity measurements with machine learning, the research unveils valuable data on sedimentary patterns and seismic activity in the region. The methodology includes creating an Apparent Geological Model (AGM) using machine learning algorithms and resistivity data interpolation. The study establishes classification criteria and models sediment distribution, identifying distinct patterns in clay, sand, and gravel. The research reveals rapid subsidence and suggests links to prehistoric seismic events with a 4000-5000 recurrence period. [ABSTRACT FROM AUTHOR]