This review provides an in-depth analysis of solar forecasting techniques and the role of Artificial Intelligence (AI) in improving their accuracy. Solar energy forecasting is crucial for integrating solar power into electricity grids and managing renewable energy resources effectively. The study highlights the importance of AI techniques, particularly Neural Networks, in solar forecasting, and discusses various methods such as supervised learning, regression, ensembles, and physics-based approaches. The integration of satellite imagery, weather predictions, and historical data enhances forecasting precision. The review emphasizes the need for standardized datasets and benchmark methods to ensure accurate evaluations and meaningful comparisons with naive forecasts. It also discusses the use of different data sources, including satellite images, all-sky imagers, sensor networks, and numerical weather prediction, for solar forecasting. The study covers a wide range of time horizons, from ultrashort intervals (1 min to 1 h) to longer durations (up to 24 h), and evaluates various error metrics to assess forecasting performance. The review also addresses the challenges of predicting solar irradiance, including ramp events and timing errors, and highlights the importance of confidence intervals and ranges in solar forecasting studies. The study concludes that AI techniques, particularly deep learning models, have shown significant potential in improving solar forecasting accuracy, and future research should focus on developing benchmarking frameworks and standardized datasets to enhance the reliability of solar forecasting models.This review provides an in-depth analysis of solar forecasting techniques and the role of Artificial Intelligence (AI) in improving their accuracy. Solar energy forecasting is crucial for integrating solar power into electricity grids and managing renewable energy resources effectively. The study highlights the importance of AI techniques, particularly Neural Networks, in solar forecasting, and discusses various methods such as supervised learning, regression, ensembles, and physics-based approaches. The integration of satellite imagery, weather predictions, and historical data enhances forecasting precision. The review emphasizes the need for standardized datasets and benchmark methods to ensure accurate evaluations and meaningful comparisons with naive forecasts. It also discusses the use of different data sources, including satellite images, all-sky imagers, sensor networks, and numerical weather prediction, for solar forecasting. The study covers a wide range of time horizons, from ultrashort intervals (1 min to 1 h) to longer durations (up to 24 h), and evaluates various error metrics to assess forecasting performance. The review also addresses the challenges of predicting solar irradiance, including ramp events and timing errors, and highlights the importance of confidence intervals and ranges in solar forecasting studies. The study concludes that AI techniques, particularly deep learning models, have shown significant potential in improving solar forecasting accuracy, and future research should focus on developing benchmarking frameworks and standardized datasets to enhance the reliability of solar forecasting models.