SQL injection attacks are a major danger to data security in application systems, leveraging weaknesses to illicitly access and change sensitive data. Traditional detection methods, such rule-based systems and supervised machine learning, struggle to adapt to new SQL injection attack strategies. This study presents an Enhanced Deep Learning Framework for SQL Injection Detection utilizing the Gated Recurrent Unit (GRU) model to overcome constraints. To discover new attack patterns, the proposed framework uses a dynamic learning process instead of static detection methods. By examining SQL query sequences, the model can distinguish between legal and malicious interactions without predefined rules or reinforcement learning. The framework's performance is assessed using a broad dataset of valid and malicious SQL queries. Experiments show a considerable increase in detection accuracy, reaching 96.65% with little false positives. The proposed system is resilient and adaptable to address the complexity of modern SQL injection threats. The results demonstrate the effectiveness of deep learning, particularly the GRU model, in detecting SQL injection attacks. This research enhances database security and lays the groundwork for future cyber-security methods in web-based applications.