In the modern era, our dependence on web applications is ubiquitous, with the user base steadily increasing each day. Many organizations depend on databases to keep track of user data or the information they submit. Structured Query Language (SQL) is frequently employed for interacting with these databases. During this attack, a malicious entity injects harmful SQL commands into the database. This poses a grave security risk, as it can lead to the theft of database contents. While it's possible to delete the entire database, this action alters the data stored within it. An attacker could exploit SQL injection to gain unauthorized entry into the entire database. Such SQL malicious attacks possible if web application is not able to adequately validate or screen the data provided by users. Various artificial intelligence techniques are utilized to identify and examine SQL injection threats, including naive Bayes, gradient boosting, support vector machines, decision trees, convolutional neural networks, etc. Our suggested approach for detection leverages neural networks, boasting superior detection rates compared to current methods.