Unmanned Aerial Vehicles (UAVs) generate extensive log data, which plays a crucial role in forensic investigations of incidents such as accidents, trespassing, and malicious activities. However, notifications are often susceptible to corruption due to hardware malfunctions, software glitches, and environmental factors. This corruption leads to missing, substituted, reordered, or distorted data, which complicates the analysis. This research introduces the predictive framework to recover errors in corrupted notifications for UAV forensic analysis (PRECN), a novel data recovery framework for notification errors in UAV forensic analysis. The proposed methodology involves systematic data pre-processing, synthetic generation of corrupted word patterns, and the application of combined similarity models. Key techniques include a customized Document Term Matrix (DTM) representation, cosine similarity, and Normalized Levenshtein Distance, integrated into a combined scoring mechanism to accurately predict base words from corrupted inputs. PRECN is evaluated on a dataset of 4977 English language UAV notifications, encompassing 46,335 words from 63 drone models, with corruption simulated across eight distinct error types. The results demonstrate PRECN’s high accuracy and robustness across a variety of error patterns, making it a valuable tool for enhancing the reliability of forensic analysis and data recovery in UAV systems.