This paper presents the performance of the grocery identification system concerning Telugu grocery items, considering both native and non-native speakers. Speech recognition for Telugu groceries presents a unique challenge due to variations in pronunciation, accent, and noise conditions. This study explores the implementation of a Gaussian mixture model (GMM) classifier in conjunction with rasta-perceptual linear prediction (RASTA-PLP) features to enhance the accuracy of Telugu grocery identification. Rasta-PLP effectively captures robust speech features by suppressing unwanted spectral variations, while GMM provides a probabilistic framework for classification. The proposed system is trained on a dataset comprising commonly used Telugu grocery names and evaluated under diverse acoustic environments. Experimental results demonstrate improved recognition performance, showcasing the effectiveness of RASTA-PLP in feature extraction and GMM in classification. This work contributes to developing efficient speech-based interfaces for regional language applications, facilitating voice-driven grocery identification systems. The recognition accuracy of the proposed system is approximately 99%, ensuring high reliability in real-world applications. This technology benefits society by aiding visually impaired individuals and non-Telugu speakers in grocery identification, enhancing accessibility and convenience. By enabling seamless voice-based interaction, promotes inclusivity and improves social equity through technological advancement.