The COVID-19 emergency has stressed the great emphasis placed on getting credible projections for use in disease management and mitigation efforts. Machine learning (ML) can be used as a very sensible tool for building these kits by analyzing big datasets to anticipate disease trends and results. This paper gives an insight into the innovation, difficulties, and prognosis for the invention and wide implementation of the ML-based COVID-19 prediction kits. We analyze and focus on the present methods where the application of ML has been proven to contribute to the accuracy of prediction rather than the classical procedures. Data acquisition and preprocessing techniques are discussed in detail along with algorithm selection and training. Feature selection and engineering (fitting) affect the kind of predictive variables you include. Accounting for deployment barriers, as well as integration issues, will be discussed along with the ethical and societal implications involved. Finally, we present the future outlook and a conclusion with a focus on the necessity of continued research efforts and collaboration in this equally critical public health sector. This improvement of an ML-based COVID-19 prediction kit marks a big step toward fighting the pandemic and predicting future outbreak events.