This dataset contains synthetic flight data generated in FlightGear for the study and early prediction of accelerated stall conditions in the Antonov An-32 aircraft. The simulations were designed to reproduce aggressive maneuvering scenarios associated with high load factors and non-linear aerodynamic behavior near stall conditions. The dataset includes multivariate time-series flight parameters such as angular rates, translational velocities, attitude angles, control surface deflections, engine variables, aerodynamic states, and other dynamic flight variables collected through a custom UDP-based telemetry system and XML communication protocols. The primary purpose of this dataset is to support research in: accelerated stall prediction, sequential machine learning models, aerospace anomaly detection, flight dynamics analysis, and synthetic data generation for aviation applications. The dataset was developed as part of an undergraduate research project focused on evaluating the feasibility of early warning systems based on sequential deep learning architectures, including LSTM, GRU, and Transformer-based models. All data were synthetically generated in simulation environments and do not correspond to real flight recorder data. Keywords: accelerated stall, flight dynamics, synthetic data, FlightGear, aerospace engineering, time-series data, machine learning, anomaly detection, aviation AI, Antonov An-32.