A rapid integration of the photovoltaic (PV) system into the distribution network grid has introduce the challenges that threaten grid stability called Reverse Power Flow (RPF). Its happen when the local demand is local power generation exceeds the demand thus causing a voltage violation that can create miscoordination in protection system and reducing the grid hosting capacity. This study examines the evolution of RPF mitigation from conventional hardware-based solutions to advanced intelligence-driven frameworks. This analysis is structured in three interrelated pillars: (1) Grid Hardware and Direct Control, a review of the contribution of smart inverters and advanced power electronic in decentralized voltage regulation. (2) Temporal Flexibility via Storage, a study of BESS application in aligning generation with the demand cycle. And (3) The Intelligence Layer, analysis on the use of the high fidelity forecasting and the artificial intelligent (AI), that include Reinforcement Learning (RL) and Graph Neural Networks (GNN), on how to enable adaptive grid management. Although a progress has been made, there are still a critical gap in term of reliability, robustness and interpretability for the AI based solutions. In additional to the challenges of integrating disparate technologies in to the topology aware cyber physical framework. This review concludes with a strategic roadmap which emphasizing physic-based machine learning, coordinate a standard on benchmarking and assessment on the sustainability.