Forecasting plays a crucial role in various fields such as business, healthcare, weather prediction, and business analytics. Traditional statistical models have been broadly used for forecasting, however, they have restrictions in handling large-scale, high-dimensional, and non-linear data. Machine learning (ML) algorithms have occurred as controlling tools for forecasting due to their ability to learn different shapes from data without unambiguous programming. This paper explores various ML algorithms used for forecasting, together with regression models, artificial neural networks, and ensemble methods. It examines how different ML techniques associate with conventional forecasting models and discusses strategies to enhance model accuracy through hybrid approaches, deep learning architectures, and big data integration. Additionally, it addresses key encounters such as model interpretability, data availability, and computational complexity. By leveraging ML, the future of weather forecasting can move towards greater accuracy, efficiency, and adaptability. We analyze their assets, tests, and applications in real-world circumstances. The study accomplishes that ML-based forecasting provides improved accuracy, adaptability, and proficiency associated to outdated methods, although challenges like over fitting, data quality, and model interpretability remain significant concerns.