Coastal and open ocean regions throughout the world are now subject to an array of toxic, harmful, or more intense algal blooms with an increasing trend of incidence over large geographical areas due to anthropogenic factors such as pollution and climate shifts. To date, detection capabilities of causative species based on remote sensing data are greatly limited because of the difficulties in interpreting the composite reflectance signal from different water features and types. In the present study, an accurate and reliable method is developed to automatically detect the onset of blooms and correctly classify the bloom species in Arabian Sea and Bay of Bengal waters using remote sensing data. A data-driven approach using machine learning algorithm is devised based on reflectance spectral signatures and tested on several MODIS-Aqua (Moderate Resolution Imaging Spectroradiometer) data for classifying the dominant water categories, including clear ocean waters devoid of sediments and algal blooms, sediment-laden coastal waters, and three major algal blooms, Trichodesmium erythraeum, Noctiluca scintillans and Cochlodinium polykrikoides. An extreme gradient boosted decision tree (XGBoost) model is chosen to improve the prediction accuracy by prevention of overfitting, which increases the scalability of the model on several unseen test data. This model was trained using 1.5 million samples and resulted in a classification accuracy of over 98%. When the results were validated using forty thousand random samples from the known blooms, an overall accuracy more than 96.8% was achieved. The applicability of the trained XGBoost model was further verified using MODIS-Aqua images, and it showed promise for successful detection and identification of well-documented blooms. The use of spectral information to classify algal blooms makes this method more robust and easily adaptable to different ocean colour sensors with a scope to accommodate other major algal blooms. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.