During the operation of submarine outfalls, wastewater is released into seawater. Given that such water contains various impurities such as bacteria, toxins, metals, and large amounts of salt and can have significant temperature variations, achieving the best possible mixing is necessary to minimize the negative effects on the marine ecosystem. In the doctoral dissertation, the behavior of submarine outfalls was analyzed using numerical models and machine learning. Therefore, four techniques were proposed that contribute to understanding behavior and reducing negative environmental impact. They provided a set of innovative methods that can be applied in construction planning, monitoring the operation of submarine outfalls, and determining their impact on the environment. Wastewater usually has a different density than seawater, which causes different buoyancy effects. Positive or negative buoyancy jets can be analyzed depending on the density of wastewater, and both cases were observed in this paper. The results of numerical simulations of buoyant jets modeled in OpenFOAM were compared with previous experimental data. Since the stated problem requires non-stationary simulations of fluid mixing with different properties, performing such simulations requires significant computer resources. Hence, an approach of reducing the number of variables was proposed, which are calculated in numerical simulations, whereby the density of wastewater is calculated based on the known salinity and temperature, and the concentration is further observed as a proportion with respect to the density. In this way, the number of differential equations to be calculated is reduced. Numerical simulations with the mentioned approach showed a very good agreement with the experimental data, and the mentioned models were used in further analysis. When designing the outfall operation, it is necessary to choose adequate flow parameters and the appropriate geometry of the outfall to minimize the environmental effects. For this reason, a methodology was developed for creating and analyzing negative buoyant jets based on machine learning and the SHAP method (Shapley Additive Explanations), whereby the database was developed on many numerical simulations. Several different flow conditions and a few machine learning models were tested. The best results were achieved using an artificial neural network, while the most influential parameter on the behavior of the jet proved to be the velocity of the jet. In order to obtain actual flow conditions and model the operation of submarine outfalls, including sea currents and other parameters, a one-way nesting procedure of the twoLiquidMixingFoamOpenFOAM model was developed in the global ROMS model (Regional Ocean Modeling System) of sea flow. The procedure was applied to a real submarine outfall in Rijeka for the summer and winter conditions, and the spread of pollution was analyzed. The results showed that this approach can be very useful for higher velocities in the lower layers of the sea, where the usual procedures that ignore the velocities of sea currents are not adequate. The application of satellite images is proposed to monitor the operation of submarine outfalls and validate numerical models. For these purposes, available methods and types of images that can be used were analyzed. A methodology was established for processing images from the Sentinel-2 satellite, which was applied to the actual outfall in Rijeka. The aforementioned enables the analysis of the work of the submarine outfall over a longer period in order to be able to plan future operations, spot any damage and monitor the spread of pollution. A comparison of recordings and environmental parameters was made. The results of the processing of satellite images were compared with the numerical model, and the location of the pollution occurrence was matched. The mentioned set of techniques is used to improve the planning and analysis of the operation of submarine outfalls, thereby influencing the reduction of pollution in the coastal marine area.