This project focuses on the development of conversational analytics and AI solutions for Seasalt.ai, aimed at analyzing real-world speech data for insights. Multiple Machine Learning models were implemented, with Convolutional Neural Networks (CNN) demonstrating the highest accuracy improvement of approximately 67%. The CNN model effectively identifies emotions from audio data clips, enhancing conversational analytics within Seasalt.ai's product SeaMeet. Extensive experiments and evaluations have been conducted to validate the effectiveness of the CNN-based approach, showcasing its potential for real-time applications such as affective computing, virtual assistants, and emotion-aware human-computer interaction.