Color image segmentation plays a significant role in the digital image domain, which partitions the image into regions based on color features. Several existing approaches, such as thresholding, clustering and edge recognition approaches, are sensitive to noisy images and struggle to offer a precise segmentation of irregular and overlapping objects. In order to address these issues, an innovative approach is proposed to boost the segmentation accuracy. Here, the novel Modified Distance-based Balanced Iterative Reducing and Clustering using Hierarchies (MD-BIRCH) approach is suggested in this research to segment the color images. Firstly, the Gaussian filtering method is employed for preprocessing the input image aimed at minimising the noise and improving the image quality. Then, the MD-BIRCH algorithm is proposed to segment the preprocessed image to ensure an effective segmentation outcome. Therefore, the proposed Modified Distance-based Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm for Color Image Segmentation (MDB-CIS) model efficiently segments the color pictures by minimizing the noise via a Gaussian filtering approach for precise segmentation.