The rapid increase in population has made renewable energy sources critically important for ensuring sustainable development and securing environmental protection for future generations. These energy sources play a significant role in reducing greenhouse gas emissions caused by fossil fuels, offering an effective solution to the multifaceted challenges posed by climate change. Renewable energy sources such as hydroelectric, biomass, geothermal energy, wind and solar not only minimize environmental impacts but also provide long-term cost advantages. Today, solar energy sources have become the focal point of research due to their contributions to reducing environmental damage and enhancing economic sustainability. In our study, solar energy was preferred considering factors such as geographical flexibility, cost advantage, developing technology, environmental sensitivity and wide application areas. The need for accurate predictions is rapidly increasing to ensure the success of sustainable energy practices. Consequently, there is an increasing demand in the energy sector for developing machine learning (ML) algorithms that can create reliable models using limited available data. Compared to traditional models, machine learning algorithms stand out due to their ability to make more accurate predictions and serve as effective tools for addressing fundamental challenges associated with renewable energy sources. The integration of fuzzy logic with ML enhances interpretability and adaptability, making it particularly useful for handling imprecise and ambiguous data in real-world applications. Fuzzy inference systems (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) play a crucial role in leveraging fuzzy logic to model uncertainty and approximate reasoning, enabling more flexible and human-like decision-making in complex and imprecise environments. This study aims to provide an extensive literature survey on the use of fuzzy inference systems in solar energy applications. The review is planned to be supported by tables and graphical representations.