The iterative process of urban development often produces fragmented renewal zones that disrupt the continuity of urban morphology, undermining both cultural identity and economic cohesion. Addressing this challenge, this study proposes a generative design framework based on Deep Convolutional Generative Adversarial Networks (DCGANs) to predict and regenerate urban morphology in alignment with existing spatial contexts. A dataset was constructed from highly urbanized city centers and used to train a DCGAN model. To evaluate the model performance, seven landscape pattern indices—LPI, PLAND, LSI, MPFD, AI, PLADJ, and NP—were employed to quantify changes in scale, shape, compactness, fragmentation, and spatial adjacency. Results show that the model accurately predicts morphological patterns and captures underlying spatial logic in developed urban areas, demonstrating strong sensitivity to local form characteristics, and enhancing the feasibility of sustainable urban renewal. Nonetheless, the model’s generalizability is constrained by inter-city morphological heterogeneity, highlighting the need for region-specific adaptation. This work contributes a data-driven approach to urban morphology research and offers a scalable framework for form-based, sustainability-oriented urban design.