In recent years, the COVID-19 pandemic has emerged as a global crisis, underscoring the importance of early detection and analysis in controlling disease outbreaks. However, due to high uncertainty and a lack of essential outbreak data, traditional models have struggled with accuracy in long-term predictions. While the literature review highlights various attempts to address this challenge, existing models still require improvement in terms of generalization and robustness. Recent studies suggest that Machine Learning (ML) techniques offer a promising approach to analyzing health-related data, enabling the identification of potential disease outbreaks, facilitating timely interventions, and ultimately reducing healthcare costs. This research seeks to evaluate the performance and predictive capabilities of various machine learning algorithms to determine the most accurate and reliable models for disease prediction. The findings aim to propose a novel framework for early outbreak detection using ML techniques and to conduct a comparative analysis of studies that have employed ML for detecting disease outbreaks.