Financial forecasting is indispensable for various financial sectors' strategic decision-making, risk management, and investment planning. Traditional statistical methods, though valuable, frequently struggle to capture the intricate, non-linear patterns embedded within financial data. In response to these limitations, Gradient Boosted Trees (GBTs) have emerged as a formidable ensemble learning technique renowned for enhancing predictive accuracy. This comprehensive review paper delves into applying GBTs in financial forecasting, scrutinizing their methodological underpinnings, distinct advantages, and comparative performance against other machine learning approaches. GBTs, with their real-world applications in financial forecasting, operate by sequentially combining multiple weak learners, typically decision trees, to iteratively refine predictions by focusing on residual errors. This iterative approach enables GBTs to effectively model complex relationships and interactions in financial datasets, outperforming traditional models like ARIMA and linear regression in many scenarios. Moreover, the paper addresses critical implementation challenges associated with GBTs, such as hyperparameter tuning and computational complexity, which are pivotal for achieving optimal performance. The review identifies promising avenues for future research, including integrating GBTs with deep learning techniques and advancements in real-time forecasting capabilities. By elucidating these aspects, this paper aims to provide insights that enhance the application and efficacy of GBTs in financial forecasting contexts.