Methods for identifying human activity have a wide range of potential applications, including security, event time detection, intelligent building environments, and human health. Current methodologies typically rely on either wave propagation or structural dynamics principles. However, force-based methods, such as the probabilistic force estimation and event localization algorithm (PFEEL), offer advantages over wave propagation methods by avoiding challenges such as multi-path fading. PFEEL utilizes a probabilistic framework to estimate the force of impacts and the event locations in the calibration space, providing a measure of uncertainty in the estimations. This paper presents a new implementation of PFEEL using a data-driven model based on Gaussian process regression (GPR). The new approach was evaluated using experimental data collected on an aluminum plate impacted at eighty-one points, with a separation of five centimeters. The results are presented as an area of localization relative to the actual impact location at different probability levels. These results can aid analysts in determining the required precision for various implementations of PFEEL. •PFEEL algorithm on an aluminum plate to accurately localize impact events.•A Gaussian Process Regression model to provide probabilistic localization and force.•PFEEL-GPR reduces modeling errors and computational costs for event detection.•Bayes to model the system’s input/output improves reliability in event detection.