<p>The Mountain Gazelle Optimizer (MGO) is a recent nature-inspired metaheuristic that has attracted growing interest due to its simplicity, flexibility, and competitive search performance. This paper presents a systematic review of 64 peer-reviewed studies published between 2022 and 2025, covering algorithmic foundations, methodological enhancements, hybrid frameworks, and application-specific adaptations of the mountain gazelle optimizer. Methodological studies mainly focus on improving convergence behavior, population diversity, and robustness through adaptive parameter control, chaotic and opposition-based strategies, and neighborhood search mechanisms. Hybrid variants integrating machine learning models, control systems, mathematical programming, and system-level frameworks further extend its capability to solve nonlinear and constrained optimization problems. Application-oriented research demonstrates the effectiveness of the optimizer in renewable energy systems, power system optimization, machine learning, internet of things and communication networks, engineering design, and construction management. Reported results consistently show that this optimizer and its variants perform competitively compared with established metaheuristic algorithms. Key challenges and future research directions are discussed to support further development and real-world adoption of the mountain gazelle optimizer-based optimization approaches.</p>