Artificial intelligence (AI) is revolutionizing ophthalmology, particularly in microsurgeries demanding precision. Current AI applications span the entire surgical process: candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance. This integration is evident in refractive surgery, keratoplasty, cataract surgery, vitreoretinal surgery, and oculoplastic surgery. Beyond surgery, AI improves diagnostic accuracy for conditions like diabetic retinopathy, age-related macular degeneration, and glaucoma, while also enabling personalized treatment and enhancing service delivery via teleophthalmology and workflow optimization. The expanding scope of AI in ophthalmology signifies its maturation from analytical to interventional tools, aligning with a broader healthcare trend towards AI-augmented procedures. The success of AI in one area, such as precise intraocular lens (IOL) calculation, holistically influences subsequent surgical steps, maximizing AI's value across the patient journey. However, widespread adoption faces challenges including data privacy, algorithmic bias, lack of standardized metrics, limited public datasets, and integration complexities. Addressing these fundamental barriers is crucial for trust, equity, and practical implementation. Future directions emphasize enhanced multimodal AI models, integrated robotics, and expanded global health initiatives through teleophthalmology to address disparities and improve patient outcomes worldwide.