A key aspect of musical analysis is the understanding of musical chords, which makes it easier to comprehend the harmonic patterns used in songs. Inspired by recent advances in audio-based music analysis, this paper presents a novel approach for chord detection using Convolutional Neural Networks (CNNs). We describe an in-depth method that can directly extract detailed chord information from audio raw data. The strategy we use makes use of CNN’s hierarchical learning capability to identify intricate patterns that exist in a variety of musical genres. The model is a flexible tool for chord recognition across a wide range of musical styles due to its ability to adjust to changes in instrumentation, tempo, and recording conditions. Our model adds to then interpretability of the underlying music structures in addition to correctly decoding chords. We are primarily focused to bridge the gap between artificial intelligence and the complexities of musical expression. This research not only advances the field of automated chord recognition but also is in itself a reflection upon the evolving relationship between technology and artistic interpretation. In the subsequent sections, we will delve into the theoretical foundations, architectural intricacies, experimental outcomes, implications for future research, offering a valuable contribution to the dynamic field of musical analysis.