The increasing demand and complexity of air traffic management (ATM) systems necessitate significant advancements in automation to ensure safety and efficiency. Artificial intelligence (AI) and machine learning (ML) are emerging as promising solutions to manage this growing complexity, offering enhanced decision-making and predictive capabilities. However, the effectiveness of ML models in ATM heavily relies on the availability of extensive, high-quality data. In many cases, such data is scarce or incomplete, which presents a major barrier for training robust models. Synthetic data generation (SDG) is a viable solution to address this, enabling the creation of realistic datasets that unlock the ML value proposition. The Terminal Maneuvering Area (TMA) is a crucial segment of airspace characterized by high traffic density and diverse trajectory types, necessitating granular data to model these scenarios accurately. The main research objective of this work was to investigate the applicability of TimeGAN in generating synthetic 4-dimensional aircraft landing trajectories capable of capturing traffic patterns in this airspace, helping to analyze airspace constraints and delay propagation. The resulting synthetic trajectories were evaluated in terms of data diversity, fidelity and usefulness. The main challenge identified during the research was the imbalance in data classes, which affected the models’ ability to accurately capture data patterns, particularly in less frequent scenarios. Generating synthetic data based on separate groupings showed promise in addressing these imbalances, although this approach was sensitive to the designation of groups. This work proves the capability of TimeGAN in generating diverse, realistic trajectories that are difficult to differentiate from real historical data.