One of the most popular frameworks for guaranteeing data privacy is differential privacy preserving statistical utility. However, its practical application faces critical challenges, including the lack of a standardized approach for selecting privacy parameters, limitations in flexibility for diverse real world scenarios, and vulnerabilities in data-dependent settings. This paper offers a unique project that tackles these issues by using an enhanced differential privacy mechanism tailored for real-world datasets. Our research introduces an adaptive method for dynamically selecting the privacy parameter (ε), maintaining the best possible balance between data utility and privacy protection. Additionally, we enhance differential privacy mechanisms to support broader applications by customizing noise injection techniques, making them more adaptable to various data types and use cases. Experimental evaluations demonstrate that our approach significantly improves privacy preservation while maintaining analytical accuracy. Furthermore, we propose a robust solution to mitigate the vulnerabilities of differential privacy in data-dependent contexts, reducing the impact of inference attacks that exploit social, behavioral, and genetic relationships within datasets. By refining existing methodologies and introducing novel adaptations, our project enhances the effectiveness of differential privacy for real-world deployment. These findings contribute to advancing privacy-preserving techniques, enabling more secure and practical data analytic solutions for sensitive data handling in a variety of sectors.