Artificial intelligence offers powerful methods for audio processing and analysis but complex workflows and required programming skills often limit access for students and domain experts like marine bioacousticians and soundscape ecologists. We present an application “Sound-AI”, a code free and interactive tool that lowers these barriers by providing users to construct and explore complete AI pipeline for audio data analysis. Starting from raw recordings, users can choose from various feature extraction techniques (MFCC, OpenL3), apply dimensionality reduction method (PCA, t-SNE, UMAP), and optionally perform unsupervised clustering (K-Means, GMM, DBSCAN). The results are displayed in an interactive 2D visualization where user can compare multiple plots by varying multiple techniques i.e. t-SNE vs PCA. Interactive plots allow selection of points or clusters of interest, visualize spectrograms in desired frequency range, and play audio clip of associated points. An integrated ‘Help’ feature provides explanation of each method (i.e. what it is, how it works and practical use in different domains like bioacoustics), fostering both conceptual understanding and practical skill. For precomputed features or embeddings, this tool also supports training and evaluating various machine learning algorithms with visual feedback. By merging accessibility, interactivity, pedagogy, and domain relevance, “Sound-AI” demystifies AI methods for interdisciplinary education and supporting research in audio analysis.