Robust automatic event detection and location is central to real‐time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML‐based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal‐to‐noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real‐time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ‐Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short‐term average over long‐term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.