This study suggests a computationalized process of determining and modifying Mughal architecture motifs and design patterns with the help of Artificial Intelligence. Mughal ornamentation (floral arabesque, jali patterns, calligraphic panels, and Pietra dura inlays) is examined with CNN models trained on transfer learning (VGG16 and ResNet50). The trained models have classification accuracy of 98.6%, which indicates that the trained models are reliable to identify motifs even in small datasets. After classification, generative tools with AI assistance are used to generate variations of motifs, which are optimized with the help of scalable textile and surface design applications using the workflow of vectors based on the CAD format. Methodology provides a systematic line of information flow between architectural heritage recording and modern fashion making allowing to incorporate Mughal  patterns into the sarees, dupattas, shawls, and heritage-based clothing. The study includes a repeatable AI- based methodology that satisfies cultural heritage, deep learning, and contemporary fashion design.