The Mediterranean basin is currently one of the most climate-vulnerable regions in the world. It is characterized by a rapid rise in temperatures, a gradual and irregular decrease in precipitation, an increase in the frequency and intensity of droughts, as well as growing human pressure linked to urbanization, intensive agriculture, and tourism. In this context, water, energy, and food systems are highly interdependent and form a complex whole often referred to as the water–energy–food (WEF) nexus. These three dimensions can no longer be managed in isolation, as each one directly influences the others: water is essential for agricultural and energy production, energy is required for the extraction, treatment, and transport of water as well as for all agri-food supply chains, while agriculture heavily relies on water and energy resources and is a cornerstone of regional food security. This interdependence is further intensified in the Mediterranean by the scarcity of freshwater resources, extreme climate variability, and the fragility of ecosystems. As a result, trade-offs between these sectors become central and particularly complex, as any decision in one domain generates cascading effects on the others, making compromises difficult and often conflictual.
In this context of systemic complexity and growing uncertainty, predictive artificial intelligence systems and decision-support tools appear as promising instruments to improve integrated management of the WEF nexus. These systems rely on advanced techniques such as machine learning, hydrological and climate modeling, multi-objective optimization, and decision support systems (DSS), sometimes complemented by digital twins capable of simulating entire territorial environments. Their main contribution lies in their ability to analyze large amounts of heterogeneous data, identify patterns invisible at the human scale, and simulate different future scenarios in order to inform decision-makers. In the case of the water–energy–food nexus, these systems can, for example, anticipate future water availability based on climate projections, optimize agricultural irrigation needs, estimate the energy demand associated with different water uses, and identify points of tension between competing sectors. They thus make it possible to transform a traditionally reactive system into a more anticipatory one oriented toward crisis prevention.
One of the major contributions of artificial intelligence lies in its ability to simultaneously optimize multiple often conflicting objectives. In Mediterranean systems, this may involve reconciling the maximization of agricultural production, the reduction of water consumption, the lowering of energy costs, and the preservation of natural ecosystems. Through multi-objective optimization algorithms, it becomes possible to explore a very large number of scenarios and identify so-called “optimal compromise” solutions that minimize overall losses while balancing sectoral priorities. In agriculture, these tools can recommend more efficient irrigation strategies, select less water-intensive crops, or encourage agroecological practices adapted to local conditions. In the energy sector, they enable the adjustment of production choices according to water constraints, for example by favoring renewable sources that are less water-dependent and optimizing storage and distribution systems.
Beyond technical optimization, these systems also help strengthen the resilience of Mediterranean ecosystems in the face of increasing climate stress. Resilience can be defined as a system’s ability to absorb shocks, adapt to disturbances, and in some cases transform itself while maintaining its essential functions. Artificial intelligence tools enhance this resilience by improving forecasting and anticipation of extreme events, particularly prolonged droughts or acute water stress episodes. They also facilitate real-time ecosystem monitoring through the integration of data from satellites, environmental sensors, and geographic information systems, enabling faster detection of soil degradation, biodiversity loss, or resource overexploitation. By providing up-to-date and precise information, these technologies support adaptive management policies capable of quickly adjusting to changes in climate and resource availability.
However, despite their significant potential, these systems also have limitations and raise several critical issues. On the technical side, their effectiveness strongly depends on the quality, availability, and granularity of the data used, which is a major challenge in many Mediterranean regions where monitoring infrastructures remain uneven. Moreover, predictive models always involve a degree of uncertainty, particularly in the face of extreme climate phenomena or socio-economic disruptions that are difficult to anticipate. On the governance side, the use of artificial intelligence may also lead to risks of technocratization of decision-making, where choices are increasingly delegated to algorithmic systems at the expense of local stakeholder participation. There is also a risk of bias in models, which may result in unevenly favorable outcomes across territories or social groups. Finally, another important risk is technological solutionism, the belief that technological innovation alone can solve problems that also require deep political, economic, and social transformations.
Thus, predictive artificial intelligence and decision-support systems offer considerable prospects for improving integrated management of the water–energy–food nexus in the Mediterranean basin and strengthening ecosystem resilience in the face of growing climate pressures. They enable better anticipation of crises, optimization of trade-offs between competing sectors, and more refined management of natural resources. However, their real effectiveness strongly depends on their integration into appropriate, transparent, and inclusive governance frameworks. Artificial intelligence cannot be seen as a standalone solution, but rather as a decision-support tool that assists public policies, collective choices, and the structural transformations essential for the sustainability of Mediterranean socio-ecological systems.