Optical Coherence Tomography (OCT) is a non-invasive imaging modality widely used in a number of medical specialities such as ophthalmology, dentistry etc. OCT generates high-resolution cross-sectional images of tissues that provide important insights for diagnosis, monitoring and treatment planning. The increasing integration of Machine Learning (ML) and Deep Learning (DL) in OCT image analysis has shown potential in automating tasks such as segmentation, anomaly detection and disease classification. However, the success of these models relies heavily on large, diverse and annotated datasets, which are often difficult to obtain due to ethical issues, high costs and labour-intensive annotation processes. Synthetic OCT data offers a promising solution to these challenges by augmenting datasets, improving model performance and balancing class distributions.