The proliferation of decentralized finance (DeFi) applications has revolutionized the traditional financial landscape by offering unprecedented levels of transparency, accessibility, and financial inclusion. However, privacy remains a critical concern, as transactions conducted on public blockchains are inherently transparent and traceable. Zero-knowledge proofs (ZKPs) present a promising solution to this problem by allowing participants to prove the validity of transactions without revealing sensitive information. In this research, we propose a novel approach to enhance transaction privacy in DeFi applications using a combination of ZKPs and deep learning techniques. Specifically, we explore the use of deep learning models to optimize the generation and verification of ZKPs, thereby improving efficiency and scalability. By training neural networks on a diverse set of transaction data, we aim to develop more robust and accurate ZKP generators capable of handling complex smart contract interactions. Additionally, we investigate the feasibility of using deep learning for transaction pattern recognition and anomaly detection, enabling DeFi platforms to detect and prevent fraudulent activities while preserving user privacy. Through extensive experimentation and evaluation, we demonstrate the effectiveness and practicality of our proposed approach in enhancing privacy and security in decentralized finance ecosystems. Our research contributes to the advancement of blockchain technology by leveraging the synergies between zero-knowledge proofs and deep learning, paving the way for more private, scalable, and user-centric DeFi applications.