In distributed data analysis, clustering plays a vital role in uncovering patterns and relationships across datasets. However, ensuring individual privacy in decentralized settings remains a challenge. Existing approaches often fall short in protecting individual privacy, requiring continuous user engagement, or being limited to specific use cases. To address these shortcomings, we propose a novel framework called nD-Laplace, leveraging Geo-Indistinguishability for privacy preservation. Our framework enables non-interactive privacy-preserving clustering while addressing challenges associated with perturbing data. We introduce grid-remapping to handle out-ranged perturbed points, ensuring clustering utility and privacy. We provide theoretical proof of adherence to generalized differential privacy principles and validate the efficacy of our methodology through real-world dataset evaluations and simulated attacks.