An essential method in signal processing, blind source separation (BSS) finds use in audio, picture, and biological signal processing, among other fields. Convolutive BSS is a particularly hard issue since it tries to separate mixed sources in cases where the mixing process is characterised by convolution. In order to meet the demands of real-time and efficient processing in applications like echo cancellation, audio source separation, and speech enhancement, this study proposes a novel VLSI (Very Large Scale Integration) architecture specifically designed for convolutive blind source separation. Convolutive BSS is accomplished by the suggested VLSI architecture with great accuracy and minimal latency by using cutting-edge algorithms and hardware optimisations. It combines many processing components, each in charge of determining and isolating the distinct source signals from the mixture that is being seen. These processing components improve separation efficiency even in the face of time-varying mixing situations by repeatedly refining source estimations using adaptive filtering approaches and complex signal processing algorithms. Adaptive parameter tuning, memory-efficient data structures, and parallel processing units are important aspects of the VLSI architecture. It is also adaptable to various computing needs and can handle varied quantities of sources, which makes it appropriate for a variety of real-world applications. The experimental findings indicate that the suggested VLSI architecture is efficient and effective in convolutive BSS settings, and that it can separate mixed sources in real time while preserving excellent signal quality. The hardware architecture is a useful tool for signal processing systems that need to extract meaningful source information from complex mixes due to its durability and scalability.