Aging brain undergoes multiple structural and functional changes. These may contribute to an increased risk of neurodegenerative disease (NDD) and other age-related diseases, highlighting the importance of assessing deviations from healthy brain aging trajectory. In this human brain study, 50 healthy adults were investigated by functional near-infrared spectroscopy (fNIRS). A resting state single channel multiwavelength fNIRS was measured from the forehead in a supine position. The subjects were divided into four age groups. A machine learning approach was utilized for age group classification by using support vector machine and random forest learners with nested cross-validation. The results suggest brain aging effects being more distinct in the oldest age group and a difference in the brain aging for the subjects of the in-between groups. Our study shows high potential for the use of fNIRS in the analysis of brain aging.