The critical role of diphthong detection in machine-translated Hindi speech recognition, focusing on improving the accuracy of Automatic Speech Recognition (ASR) systems is proposed here. The proposed system consists of (a) a speech-to-text model Indic Wav2Vec 2.0 (b) Hindi-to-English translation using GoogleTrans (c) phonetic syllable detection using CMU pronouncing library and (d) IPA-based visualization of detected diphthongs. The work addresses issues like pronunciation variability, accent differences, co-articulation effects, and the lack of labeled datasets. It also discusses the potential of deep learning (DL) to address these challenges, leveraging artificial neural networks (ANNs) and deep neural networks (DNNs) for improved detection. In addition, limitations such as cascading errors and loss of phonetic information during translation are also discussed in this work. The endings aim to contribute to the development of more accurate and robust ASR systems, particularly for Hindi and other languages with complex phonetic structures. This research contributes to phonetic-aware multilingual speech processing, with applications in education, linguistics, and AI-powered voice assistants.