Alkaloids are naturally occurring metabolites with a wide variety of pharmacological activities and applications in science, particularly in medicinal chemistry as anti-inflammatory drugs. Since they could be labelled as active or inactive compounds against the inflammatory biological response, the aim of this work was the calibration of quantitative structure-activity relationships (QSARs) based on machine learning classifiers to predict anti-inflammatory activity on the basis of the molecular structures of alkaloids. The dataset of 100 alkaloids (58 active and 42 inactive) was retrieved from two systematic reviews. Molecules were properly curated and the molecular geometry of compounds was optimized by the semi-empirical method (PM3) to calculate molecular descriptors, binary fingerprints (extended-connectivity fingerprints and path fingerprints) and MACCS (Molecular ACCess System) structural keys. Then, we calibrated QSAR models based on well-known linear and non-linear machine learning classifiers, i.e., partial least squares discriminant analysis (PLSDA), random forests (RF), adaptive boosting (AdaBoost), k-nearest neighbors (kNN), N-nearest neighbors (N3) and binned nearest neighbors (BNN). For validation purposes, the dataset was randomly split into training set and test set in a proportion of 70/30. When using molecular descriptors, genetic algorithms-variable subset selection (GAs-VSS) were used for the supervised feature selection. During the calibration of the models, a five-fold venetian blinds cross-validation was used to optimize the classifier parameters and to control the presence of overfitting. The performance of the models was quantified by means of the non-error rate (NER) statistical parameter.