This systematic review assesses recent advances in cardiac pacing algorithms and considers future perspectives that might be provided by artificial intelligence and reinforcement learning for the optimization of cardiac stimulation. Cardiac pacing is a cardinal treatment for rhythm disorders to maintain optimal cardiac function, but the optimization of pacing strategies remains a significant challenge. A total of 32 articles were identified through the thorough literature search in both PubMed and IEEE Xplore; 18 studies are relevant according to the PRISMA guideline. Results indicated that over these years, there has been great improvement in cardiac pacing: reactive pacing and cardiac resynchronization therapy contribute to the regulation of cardiac rhythm. However, high costs and a need for specialized skills have remained key challenges. The integration of AI and RL in pacing optimization shows some very promising opportunities for personalized treatment that shall be realized with robust algorithms, real-time data management, and serious ethical frameworks. Although reasonable progress has been made so far, continued research and interdisciplinary collaboration are required fully to realize the benefits of AI-optimized cardiac pacing that improve clinical outcomes in patients with rhythm disorders.