## 6 Conclusions  
Nitrogen leachate prediction models yield satisfactory results as a multi-component tool that co-processes data acquired from site characteristics (e.g., soil texture and depth, terrain elevation, meteorological data (e.g., precipitation, temperature, etc.) in long time series. Microbial communities of the soil and eco-physiological parameters (e.g., canopy light level, maximum photosynthetic activity) are taken into deeper consideration as critical paths for nutrient uptake in the seeded plants' root zone, which regulates nitrogen surplus to be leached to deeper soil substrates. Soil water simulation models are valuable tools for addressing soil nitrogen pollution and contributing soil nutrient decision strategies as regards crop and fertigation management, e.g., APSIM in Australia. Nitrate-water simulation models in soil profiles require a great number of input data, some of which are to be measured with high accuracy at the field scale, a situation that is not always feasible. Thus, dynamic approach model systems were developed to overcome complex processes resulting from numerous interactions. The vast majority of the models examined were process-based or conceptual models. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferable, demonstrate certain advantages since they do not require extensive calibration database information, which in many cases is unavailable. Artificial intelligence and machine learning techniques extend even more prediction capability, offering great potential for accurate water quality prediction without time series use acquired from long period measurement and databases. AI combined with ML algorithms enables the analysis of vast amounts of bulk datasets more efficiently than traditional methods and point out unidentified interrelations and patterns that were up to now unnoticed. Thus, predictive capability is enhanced. Other potential improvements are the refinement of already-used empirical and deterministic models in such a way that leachate movement forecasting might be improved. AI technology boosts sensor integration and facilitates real-time monitoring of soil certain characteristics to predict leachate quality and other environmental parameters that affect the prompt soil water integration risk assessment management and policy making. Furthermore, AI eases uncertainty incorporation into models and incurs significant prediction improvements of potential impacts on ecosystems over time. AI tools are flexible when simulating a great variety of checking scenarios, facilitating decision support systems. One more important aspect of AI techniques is the enhanced ability in model validation/calibration by making use of ML techniques, which allow prompt results with fast parameter iteration and less manual effort. Hybrid structural chemical agents' prediction models facilitate researchers to encompass even more profound sophisticated tools regarding climatic change scenarios, greenhouse gases in the atmosphere, combined with soil surface temperature, and proceed to climatic analyses, synthetic weather predictions and support more accurate crop assessment yields, countermeasure decision making against extreme weather conditions, secure crop yield, and deeper understanding of crop physiology. Intense anthropogenic pressure and climate change with prolonged dry spells incur saline wedge penetration phenomena in the coastal low water table, resulting in water quality devaluation and soft water consumption limitations. Coastline water management involves integrated modeling systems that successfully couple surface and groundwater hydrology, reservoir operation, agronomic/crop planning, and nitrate leaching to address aquifer nitrate transport and seawater intrusion and resilient adaptation plans. Stochastic modeling is widely used as a fundamental approach tool to embed uncertainty in long-term model-based decisions.