Combining solar, wind, diesel, and storage units offers a resilient and sustainable solution to the growing energy demands of remote and urban areas. Yet, managing such diverse sources poses significant challenges due to variability, intermittency, and the complicated interaction between generation and consumption. This paper presents a critical overview of hybrid energy system (HES) management approaches, starting with rule-based strategies to advanced optimization and machine learning techniques. Traditional methods, while simple and intuitive, often lack adaptability and scalability. Conversely, model predictive control (MPC), metaheuristic algorithms, and reinforcement learning frameworks demonstrate remarkable potential for dynamic decision-making and real-time optimization. We analyze the strengths, limitations, and application contexts of each method, highlighting emerging trends towards autonomous, self-learning energy management systems. Summarizing recent advancements, this work serves as a foundational study to guide researchers and practitioners towards optimal design and more efficient hybrid systems adapted to future energy perspectives.