The Deep Learning-Driven Wall Visualiser for Realistic Colour and Texture Placement, a revolutionary method presented in this research, is intended to completely transform the visualisation and customisation of interior spaces. Leveraging the power of Convolutional Neural Networks (CNNs) and advanced image processing techniques, this system enables users to experiment with different colours and textures on their walls, providing a highly realistic preview of potential room designs. The proposed method utilises “DeepLabV3,” a state-of-the-art CNN architecture known for its ability to perform precise object and image segmentation. Trained on the “ADE20K” dataset, which comprises 25,000 images across 150 diverse classes, DeepLabV3 is adept at segmenting complex indoor scenes into distinct regions, facilitating accurate and seamless colour and texture replacement. This approach enhances visualisation realism by effectively capturing the nuances of lighting, shadows, and surface textures, ensuring that the applied colours and textures blend naturally with existing room features. The system’s ability to handle various interior settings, from simple to highly detailed environments, sets it apart from existing wall visualizer methods, which often struggle with maintaining realism and accuracy. Performance evaluations demonstrate that this model significantly outperforms traditional techniques in terms of visual fidelity and user satisfaction, making it a valuable tool for both interior designers and homeowners. The results suggest that the Deep Learning-Driven Wall Visualizer not only improves the aesthetic decision-making process but also reduces the time and cost associated with physical trials and errors in room decoration.