Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture.

Previous Euphrates and regulated-river forecasting studies have often compared algorithms without quantifying how far prediction can proceed when reservoir releases, storage, withdrawals, and allocation rules are unavailable. This study tested that information limit for monthly discharge at Haditha, Abbasiya, Kufa, and Nasiriyah using station-specific discharge records within 1980–2023 and observed rainfall/temperature from Iraqi meteorological sources. ARIMA, ARIMAX, SARIMAX, Random Forest, and XGBoost were arranged as a stepwise information hierarchy: persistence, climate, seasonality, nonlinear learning, and open-loop recursive prediction. Five CMIP6 GCMs (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0) under SSP2–4.5 and SSP5–8.5 were downscaled with LARS-WG 8 and used only to generate climate-conditioned scenarios for 2021–2060. Performance limits were quantified using NSE, RMSE, KGE, benchmark comparison, recursive degradation, and SHAP attribution. Statistical skill was weak except at Abbasiya (SARIMAX NSE = 0.281; RMSE = 35.98 m³ s⁻¹), while Random Forest improved one-step Abbasiya skill (NSE = 0.639) but recursive skill declined at most stations. Hydrological memory contributed 70.35%, 69.38%, 65.45%, and 43.51% at Abbasiya, Haditha, Kufa, and Nasiriyah, respectively, indicating that antecedent flow carries more predictive information than local climate within the available dataset. Projected mean changes were − 14.55/ − 14.85%, + 8.62/ + 8.40%, + 23.04/ + 23.48%, and − 17.27/ − 17.33% under SSP2–4.5/SSP5–8.5, but these are low-confidence conditional scenarios where validation was weak. For Iraqi water allocation, drought preparedness, and transboundary management, operational implementation requires release, storage, inflow, diversion, and allocation data.