<p>The Gazelle Optimization Algorithm (GOA) is a swarm-based metaheuristic inspired by the agile and adaptive movement of gazelles, designed to solve complex optimization problems. GOA has attracted increasing attention in science and engineering owing to its reported simplicity and promising performance across various optimization applications. Its promising performance has led to the development of numerous variants and enhancement strategies, including improved, hybrid, and multi-objective models. This paper presents a comprehensive survey of GOA, focusing on its variants, enhancement mechanisms, and applications across engineering and scientific domains. A Systematic Literature Review (SLR) methodology is employed to ensure a structured and transparent analysis. A total of 73 primary studies are systematically analyzed and classified based on algorithmic modifications, hybridization techniques, and application areas. The results show that GOA has been widely applied in seven major domains, particularly in energy systems, machine learning, and engineering optimization. Among the existing studies, enhancement-based variants dominate the literature, followed by hybrid approaches and multi-objective extensions. However, GOA still faces several challenges, including premature convergence, limited theoretical analysis, and scalability issues. This survey provides a structured overview of GOA developments and highlights future research directions for advancing robust and scalable optimization methods.</p>