Over the past decades, the process of monitoring bioeconomy has witnessed a significant transformation, marked by a dynamic evolution of analysis instruments and algorithms. Our study explores monitoring practices on a global scale, spanning from traditional indicators to cutting-edge AI-enabled approaches. Initially reliant on classic metrics, the bioeconomy analysis landscape has transitioned towards a more sophisticated paradigm, incorporating advanced tools for data collection, processing, and analysis. Also, the integration of artificial intelligence has empowered researchers to get actionable insights from vast and complex datasets. The results will describe the chronological progression of monitoring methodologies, from the emergence of statistical models, by introducing regression analysis, time series methods and predictive modelling, to the subsequent integration of machine learning, reaching the era of artificial intelligence and big data. This will provide a better understanding of the evolution of bioeconomy and a state-of-the-art of the AI applications in the field, offering also insights into the historical development of monitoring methodologies.