Maritime transportation plays a vital role in global trade; however, the sparse, noisy, and incomplete nature of data from the maritime Automatic Identification System (AIS) presents challenges for predictive analytics and hinders research progress in this field. This project investigates the effectiveness of data augmentation (DA) techniques in improving deep learning (DL) models for two tasks: vessel type classification and trajectory prediction. To address the lack of systematic comparisons among DA methods, this study proposes a modular pipeline that integrates AIS data preprocessing, synthetic data generation utilizing three levels of DA techniques, and subsequent statistical analysis using the Wilcoxon rank-sum and Kruskal-Wallis tests. The DA techniques range from a simple k-Means approach to an advanced method employing Variational Autoencoders, including a domain-specific approach that integrates geographical noise. Controlled experiments demonstrated that DA improved the DL model’s predictive capability and indicated significant differences among the techniques, with domain-specific methods showing particular promise. These findings assist in selecting appropriate DA techniques for specific tasks, and the pipeline lays a foundation for practical applications that can be flexibly adapted to other tasks or DA methods.