Closed-loop neuromodulation (CLNM) has emerged as a transformative approach for treating neurological disorders, enabling precise and adaptive interventions through real-time monitoring and modulation of neural activity. Although advancements in artiffcial intelligence (AI) have unlocked new possibilities for more accurate closed-loop systems, and signiffcant progress has been made in this area within academia, challenges persist in translating these technologies into clinical practice. Similarly, integrated circuits (IC) have been pivotal in optimizing power consumption, latency, and device miniaturization. However, further innovations are still required to meet the stringent demands of clinical environments. This review not only provides an overview of state-of-the-art AI-driven algorithms and IC-enabled technologies that are reshaping the neuromodulation landscape but also emphasizes a clinical practice-oriented perspective. Through analysis of related clinical trials, we highlight both the effectiveness and the obstacles of implementing these technologies in clinical settings. Finally, we propose a roadmap for integrating AI and IC innovations to address unmet clinical needs, offering insights into the future of CLNM.