Multi-modal data analysis presents formidable challenges, as developing effective methods to capture correlations among different modalities remains an ongoing pursuit. In this study, we address multi-modal sentiment analysis through a novel quantum perspective. We propose that quantum principles, such as superposition, entanglement, and interference, offer a more comprehensive framework for capturing not only the cross-modal interactions between text, acoustics, and visuals but also the intricate relations within each modality. To empirically evaluate our approach, we employ the CMUMOSEI dataset as our testbed and utilize Qiskit by IBM to run our experiments on a quantum computer. Our proposed Quantum-Enhanced Multi-Modal Analysis Framework (QeMMA) showcases its significant potential by surpassing the baseline by 3.52% and 10.14% in terms of accuracy and F1 score, respectively, highlighting the promise of quantum-inspired methodologies.