Artificial intelligence (AI) is transforming molecular catalysis by addressing long-standing challenges in retrosynthetic design, catalyst design, reaction development, and autonomous experimentation. AI-powered tools enable chemists to explore high-dimensional chemical spaces, optimize reaction conditions, and accelerate novel reaction discovery with unparalleled efficiency and precision. These innovations are reshaping traditional workflows, transitioning from expert-driven, labor-intensive methodologies to intelligence-guided, data-driven processes. Despite these transformative achievements, significant challenges persist. Critical issues include the demand for high-quality, reliable datasets, the seamless integration of domain-specific chemical knowledge into AI models, and the discrepancy between model predictions and experimental validation. Addressing these barriers is essential to fully unlock AI's potential in molecular catalysis. This review explores recent advancements, enduring challenges, and emerging opportunities in AI-driven molecular catalysis. By focusing on real-world applications and highlighting representative studies, it aims to provide a clear and forward-looking perspective on how AI is redefining the field and paving the way for the next generation of chemical discovery.