Climate finance is critical for addressing the multifaceted challenges of climate change, encompassing mitigation, adaptation, and environmental sustainability. This study aims to analyze the determinants of climate finance allocation across these dimensions and accurately estimate climate finance flows using an advanced machine learning approach. Climate Finance BERT (Bidirectional Encoder Representations from Transformers) is employed to classify development finance projects, distinguishing those that contribute to climate mitigation, adaptation, and environmental objectives. By examining a comprehensive dataset of development finance projects, this study identifies key factors influencing the allocation of climate finance. This work reveals significant patterns in climate finance distribution. This research contributes to the growing field of climate finance by offering a robust analytical framework for assessing the determinants of climate finance and proposing a scalable solution for monitoring financial flows aimed at addressing climate change in its entirety. The insights gained have important implications for policymakers and stakeholders striving to optimize the allocation of climate finance to support global sustainability and resilience goals.