The growing global demand for clean and sustainable energy has led to a significant increase in the deployment of solar energy systems. Solar power is one of the most environmentally friendly renewable energy sources. The incorporation of solar energy into the electrical grid has grown significantly in recent years due to its sustainability and environmental benefits. Solar power forecasting refers to predicting the amount of electricity a solar panel or solar farm will generate in the future, based on numerous factors, including weather conditions, geographical location and time of day. However, due to its dependence on weather conditions, solar power generation exhibits inherent variability, posing challenges for seamless integration into energy grids. Consequently, precise solar energy forecasting plays a vital role in energy management. With the increasing adoption of solar power, accurate forecasting techniques play a crucial role for ensuring stability of the grid, optimizing energy production and minimizing operational expenses. Traditional solar power forecasting methods, such as statistical time-series models, rely heavily on historical data and assume linear relationships between variables. This paper explores the application of machine learning techniques in solar power forecasting, focusing on their advantages over traditional statistical methods. Additionally, the paper discusses the significance of data preprocessing and model evaluation in achieving high accuracy. The paper also highlights the challenges faced in solar power forecasting and suggests future research directions to improve forecasting methods.