This paper presents a novel multi-modal speech analysis system that integrates deep learning architectures and “Natural Language Processing (NLP)” techniques to address the limitations of traditional speech evaluation approaches. The proposed system combines a custom “Long Short-Term Memory (LSTM)” based neural network for temporal pattern analysis, TF-IDF vectorization for keyword extraction, real-time spectrogram analysis for acoustic feature extraction, and a dynamic content fetching system from Wikipedia datasets. The system demonstrates enhanced accuracy in content relevance detection, real-time processing with minimal latency, and a scalable architecture suitable for diverse applications, including educational, professional, and research contexts. The study highlights the system’s ability to bridge the difference in the theoretical abilities of speech analysis and their practical implementation. The proposed methodology contributes to the advancement of speech analysis by addressing the challenges of insufficient integration between acoustic and semantic analysis, static reference materials, and limited scalability.