Accurate forecasting of vessel movements enhances maritime safety, enables fuel-efficient routing, andsupportsthedevelopment of autonomous ships. Traditional rule-based and statistical methods struggle in dynamic maritime environments, especiallywhen Automatic Identification System (AIS) data contains noise, gaps, or inaccuracies. This paper explores the applicationof generativeAI models—specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)—for vessel trajectoryprediction. Experimental results demonstrate that conditional GANs combined with random forest conditioningreduceaveragedisplacement error by approximately 38% compared to baseline LSTM models while delivering probabilistic multi-pathforecasts.Thestudy also addresses practical challenges such as real-time latency, training stability, model interpretability, and deployment constraints,offering directions for future intelligent maritime systems that improve decision-making in commercial shipping and naval defence.Keywords: Ship trajectory prediction, Generative Artificial Intelligence, AIS, GANs, VAEs