Posterior capsule opacification (PCO) is the most common long-term complication of cataract surgery, affecting up to a fifth of patients by 5 years and often requiring Nd:YAG laser capsulotomy. Clinical decisions on intervention depend on subjective evaluations and carry risks such as intraocular pressure fluctuations, cystoid macular edema or retinal detachment. Artificial intelligence (AI) enables detection and classification of PCO severity from retroillumination photographs, OCT and Scheimpflug tomography, risk stratification and laser dosing support. AI models achieve expert level performance, e.g. AUC 0.97 in detecting vision-threatening PCO, correlation r ≈ 0.83 in severity scores, and C-index ≈ 0.87 in capsulotomy risk nomograms. Techniques such as heat maps increase the interpretability of models and reduce physician bias. Challenges include the creation of large multicenter datasets, prospective validation, regulation, and integration into health records. Future directions include multimodal data fusion, intraoperative systems, and home monitoring via smartphones. AI can thus optimize PCO care, minimize unnecessary interventions and increase safety.