Sanskrit, a linguistically rich classical language, presents significant challenges for Automatic Speech Recognition (ASR) due to its intricate phonetic structures, complex morphology, and variability in pronunciation. Existing ASR systems often struggle with these aspects, particularly in verse-based corpora, where prosodic features play a vital role. Additionally, the scarcity of annotated datasets limits advancements in this domain. To address these issues, we introduce Sabdavrndam (), a specialized low-resource dataset of Sanskrit verses enriched with prosodic information. By integrating prosodic features, such as pitch and energy, into the ASR pipeline alongside MFCC features, we aim to enhance recognition performance for verse-based corpora. Our experiments using state-of-the-art ASR models reveal that prosodic features provide valuable contextual information but also introduce additional complexity, impacting error rates. These findings underscore the potential of prosodic features while highlighting the need for more effective integration strategies.