This work explores the intersection of code-mixing and disfluency in bilingual speech and text, with a focus on understanding how large language models (LLMs) handle code-mixed disfluent utterances. One of the primary objectives is to explore LLMs’ ability to generate code-mixed disfluent sentences and to address the lack of high-quality code-mixed disfluent corpora, particularly for Indic languages. We aim to compare the performance of LLM-based approaches with traditional disfluency detection methods and to develop novel metrics for quantitatively assessing disfluency phenomena. Additionally, we investigate the relationship between code-mixing and disfluency, exploring how factors such as switching frequency and direction influence the occurrence of disfluencies. By analyzing these intriguing dynamics, we seek to gain a deeper understanding of the mutual influence between code-mixing and disfluency in multilingual speech.