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The COVID-19 pandemic has had a significant global impact, affecting public health, economies, and social structures. Accurate forecasting of the spread and severity of the disease has become crucial for effective decision-making and resource allocation. Machine learning techniques have emerged as powerful tools for COVID-19 forecasting due to their ability to analyze complex data patterns and make predictions. In this review paper, we provide an overview of the stateof-the-art machine learning approaches employed for COVID-19 forecasting, highlighting their strengths, limitations, and future directions. We discuss the different data sources used, feature engineering techniques, modelling strategies, and evaluation metrics employed in COVID-19 forecasting research. Additionally, we examine the challenges associated with COVID-19 forecasting, including data quality issues, model interpretability, and ethical considerations. We conclude by outlining potential areas for future research and emphasizing the importance of collaboration and data sharing to improve the accuracy and reliability of COVID-19 forecasting models.