The paper presents the results of the research on oil spill detection using machine learning methods such as Support Vector Machine (SVM) for classification of infrared images and Logistic regression for water quality parameters. This paper focuses on real time detection of oil spills using infrared images and water quality data obtained by RPA equipped with multi-sensor payload. The developed Naïve Bayes (NB), SVM and Logistic regression classification models for prediction of oil spill have been successfully tested in real experiment conditions. All developed classification models were tuned using grid search method and main tuning parameters to determine the optimal parameters. The proposed complex algorithm for identification of oil spills using infrared images and water quality parameters is evaluated by experiments in real environment conditions. The proposed algorithm is based on the binary SVM and NB classification of infrared images and the classification of water quality parameters using the machine learning method logistic regression allows to rapidly and with high accuracy identify any oil pollution of water. Proposed complex algorithm achieves higher accuracy and efficiency; moreover, the developed machine learning models will further reduce the probability of human error and save man-hours of work.