Regarding the determination of the number of components (M) in a Gaussian mixture model (GMM), this study proposes a novel method for adaptively locating an optimal value of M when using a GMM to fit a given dataset; this method avoids underfitting and overfitting due to an unreasonable manually specified interval. The major contributions of this study are highlighted: (1) An adaptive interval for M (denoted as M∈[MMinAda,MMaxAda]) based on two procedures of a novel method, the modified incremental Gaussian mixture model (MIGMM), is determined via an adjustable parameter β. (2) Considering some typical criteria, the optimal number M within the obtained adaptive interval [MMinAda,MMaxAda], MOptAda, is ultimately determined. Regarding the adaptive interval, extensive experiments with typical synthetic datasets show that [MMinAda,MMaxAda], corresponding to the parameter [βMin=10−11,βMax=10−2], is determined. The performance of the MOptAda determination based on several typical criteria is evaluated on both synthetic and real-world datasets.