Large volumes of data are kept in databases and retrieved using various querying languages in the current era of information explosion. The biggest challenge experienced by users is acquiring knowledge of these querying languages and comprehending their syntax. This issue can be solved by allowing users to query databases in natural language, which makes it possible for non-technical people to access and change data. Rule-based algorithms encounter several difficulties in converting natural language to SQL queries, including comprehending descriptive values and recognizing incomplete or inferred data values. This study proposes a solution, SQLGenie, that enhances accuracy and usability by converting natural language queries into SQL using advanced natural language processing algorithms. A detailed and complex dataset evaluates and compares with current approaches showing that the suggested solution significantly improves query accuracy and user accessibility.