The study of rosette jets in crossflow is essential for understanding the transport and mixing of hot and saline discharges in crossflow ambient. Conventional approaches utilize physical experiments and Computational Fluid Dynamics (CFD) for study, which are both costly and time-consuming. This highlights the necessity for more time- and cost-efficient models, such as machine learning techniques, which offer a promising alternative for achieving accurate and efficient predictions. This study aims to evaluate the performance of machine learning algorithms using datasets generated by the CFD tool OpenFOAM, validated by experimental results. The datasets included key parameters influencing the jet trajectory, such as jet-to-ambient velocity ratios, Reynolds number, jet angle, and jet trajectory. Various machine learning algorithms, including support vector machines (SVM), Extreme Learning Machine (ELM), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were trained and tested using these datasets. The models’ performance was evaluated based on their predictive accuracy and computational efficiency. The machine learning models were developed to predict the jet trajectory based on the jet-to-ambient velocity ratios, Reynolds number, dimensionless jet angle. The performance of these models was assessed using multiple statistical metrics, and the results were benchmarked against the previous study’s findings. The machine learning models demonstrated varying degrees of success in predicting the jet trajectory. The results were compared against the previous study, showing that some machine learning models effectively captured the complex dynamics of rosette jet trajectories, validating the models’ robust generalization capabilities. These models provide a rapid and feasible alternative to traditional CFD methods for accurately predicting the trajectories of rosette jets, which support the design and environmental assessment of coastal outfall systems.