Synthetic data generation is a strong tool for solving problems in many fields, particularly in the financial sector. With an emphasis on financial applications including strategy testing, risk management, and identifying fraud, this study examines and evaluates the integration of synthetic data in time series. We look at various basic strategies that help create realistic financial time series, effectively replicating market dynamics and statistical characteristics, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and agent-based models. Furthermore, we address privacy concerns related to synthetic data, emphasizing the importance of balancing its utility with data protection. As the demand for transparent models and high-quality data grows, synthetic data offers a way to enrich datasets, enhance machine learning model performance, and ensure compliance with legal requirements. The paper also emphasizes the promise of synthetic data in increasing model robustness and facilitating secure data exchange, while highlighting the difficulties in confirming the data’s authenticity and quality. Finally, we explore future prospects and challenges in this field.