Abstract: Amidst the COVID-19 pandemic, astute public health interventions, including mobility constraints, are paramount. The bedrock of such strategies lies in the precision of forecasting models. Harnessing data from the Cantabrian Health Service, this study critically evaluates and contrasts time series analysis and cutting-edge machine learning techniques in predicting 30-day COVID-19 case trajectories. Additionally, it demystifies the technological scaffolding and methodologies of the Cantabrian Institute of Statistics` web portal for streamlined collation and display of socio-health indicators. The analysis underscores the indispensability and acumen of predictive modeling in steering agile responses to public health crises

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