Crop type classification is crucial for policymaking and precision agriculture applications. This study aimed to develop a parcel-based maize (Zea mays L.) extraction approach using Sentinel 2A-derived spectral indices and machine learning (ML) in two distinct study sites: Yeşilova and Ormankadı villages in Bursa Province, Turkey. Employing 13 widely recognized spectral indices, the investigation implemented 4 ML classifiers: support vector machines, random forest, K-nearest neighbors, and bootstrap aggregating. The training-test methodology was explored using two scenarios: Yeşilova as the training set and Ormankadı as the test set, and vice versa. The models calibrated on Yeşilova and validated on Ormankadı maintained the accuracy of the model, with an overall accuracy (OA) ranging from 79.3% to 89.9%, precision between 72.8% and 80.1%, recall between 82.1% and 84.9%, F1-score between 77.4% and 82.2%, and a Matthews correlation coefficient (MCC) ranging from 58.9% to 68.3%. Furthermore, the models consistently demonstrated good performance when Ormankadı served as the training set and Yeşilova as the test set, with commendable OA (78.7% to 84.8%), precision (85.5% to 88.0%), recall (88.0% to 91.1%), F1-score (86.2% to 89.5%), and MCC (68.2% to 76.0%). This study demonstrated the potential of using high-resolution remote sensing and ML for effective maize crop extraction using diverse datasets.