Reticular framework materials, distinguished by their precisely engineered architectures and highly tailorable functional environments, have emerged as a versatile platform for applications in energy storage, drug delivery, and environmental remediation. However, the complexity of their synthetic routes and the vast diversity of accessible topologies and compositions still pose substantial obstacles to rational design and large-scale production. In recent years, artificial intelligence (AI), in particular transformer-based models and large language models, has begun to transform reticular chemistry by enabling large-scale data mining, accurate prediction of materials properties, and algorithmic guidance for experimental design. This review summarizes the disruptive impact of AI on the discovery and synthesis of reticular framework materials, with a focus on its roles in structural design, performance prediction, and optimization of synthetic conditions. We further discuss the deep integration of AI with automated experimental platforms to build autonomous laboratories capable of generating experimental protocols, adaptively adjusting reaction parameters, and iteratively refining conditions based on real-time feedback. These developments not only accelerate the discovery cycle and improve experimental reproducibility, but also greatly expand the accessible design space of reticular frameworks. The close coupling between AI methodologies and laboratory automation is expected to steer the field toward a new research paradigm that is more intelligent, efficient, and predictive, and that enables genuinely innovation-driven discovery and synthesis.