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Agricultural Drought Risk constitutes one of the most significant and long-term damaging impacts of climate change, primarily contributing to food insecurity. Despite the large number of previous research activities on drought risk management, some countries remain excluded from the global drought studies while vulnerable communities are still exposed to famine and livelihood loss. This critical gap prove that the applied drought assessment techniques faced successive refinements over time including dataset and methodologies but exhibit notable limitation regarding both spatial assessment and theoretical consistency.The present study examines a comparative analysis of agricultural drought risk across two Tunisian watersheds, Medjerda and Merguellil, which are characterized by distinct climatological conditions. The analytical framework integrated the three core components of agricultural drought risk: hazard, vulnerability, and exposure while adopting a resource nexus perspective to capture the interdependencies among the selected indicators of each component. Drought indicators were collected from remotely sensed data over the period 2016-2024 considered as the latest drought period in Tunisia. The hazard indicators were represented by Precipitation condition index (PCI), Temperature condition index (TCI), Vegetation condition index (VCI) and Soil moisture condition index (SMCI). The vulnerability indicators included Runoff, Ground Water (GW), Primary Productivity (NPP) and Nighttime Light (NL). The exposure indicators were cropping area and population density. All indicators were normalized to ensure integration within drought analysis framework. This study employed two temporal lags initially addressing the short-term dynamics of drought hazard on a monthly scale followed by yearly assessment of drought risk components. The combination process of drought indicators was conducted by three objective weighting techniques: Principal Component Analysis (PCA), Gaussian Mixture Model (GMM) and Entropy to create time series of drought risk maps.The spatial structure of obtained drought risk maps was analyzed using spatial pattern indices, including the Gini Index, along with four landscape metrics: Number of Patches (NP), Landscape Shape Index (LSI), Shannon’s Diversity Index (SHDI), and Contagion Index (CONTAG). These indices were considered as objective functions within multiple Pareto optimization scenarios to identify the most relevant spatial configuration of drought risk maps. The optimization results provided robust evidence indicating that the entropy-based approach was the most effective method in drought risk monitoring. The Medjerda watershed, which is characterized by sub-humid regime, faced strong drought variability with a severe drought period recorded in 2023, while drought risk trend remained gradual in the semi-arid watershed, Merguellil, showing slight change in 2022 and 2023.The drought assessment determined the contribution of drought indicators in creating each component, the highest weight was assigned to VCI within monthly and yearly hazard component. Considering the vulnerability component, NPP exhibited the highest contribution followed by GW in the case of Medjerda and NL in the case of Merguellil. The cropping area had highest weight within exposure component. The results offer an objective and reliable assessment of the temporal drought risk variability and quantitatively reveal the climate–water–food nexus shaping drought risk. Overall, the study confirms the viability of using integrated risk assessment for sustainable water-use in agriculture.