Artificial intelligence (AI) is transforming landscape architecture by enhancing visualization processes and streamlining workflows. This study explores the potential of AI-driven tools, particularly text-to-image diffusion models such as Stable Diffusion and platforms such as ControlNet and LookXAI, in comparison with that of traditional human rendering techniques. Using “The Meadow” an award-winning landscape project, as a case study, AI-generated versus human-rendered visuals are evaluated on the basis of criteria such as geometric accuracy, material fidelity, and planting realism. Quantitative metrics, including cosine similarity and the structural similarity index (SSIM), are employed to assess the output quality. The results indicate that while AI tools show promise in replicating complex spatial geometries and plant diversity, limitations such as material inconsistencies and biased outputs persist. Ethical considerations, including transparency, data bias, and accessibility, are discussed to ensure the equitable adoption of AI tools in landscape architecture. The study concludes with recommendations for integrating AI methods into design workflows, emphasizing the need for hybrid approaches that balance efficiency with artistic control. By advancing AI applications in the field, this study aims to optimize design processes and improve client collaboration.