Grass-based biomes are one of the world’s most important ecosystems and provide numerous crucial functions and services, yet remain as one of the most vulnerable ecosystems today. In particular, the distribution of Plant Functional Types (PFT) between C3 grasses and C4 grasses are expected to shift dramatically due to grass’ sensitivity to environmental factors. This study analyzed the potential shifts in the spatiotemporal distribution of grass PFTs and its correlations with environmental factors within the North American Great Plains. A set of twelve phenological metrics were annually extracted from the seasonal Normalized Difference Vegetation Index (NDVI) profiles along five training clusters and three testing latitudinal transects (East, Central, West) between the years of 2010 and 2020. Measurements of the phenological metric values from the training sites were used as features to construct several classifiers for predicting site PFT classification (C3-majority or C4-majority). The three ensemble method classifiers (Random Forest, Adaboost, and Gradient Boost) demonstrated the highest classification accuracy of the six classifiers constructed. Amongst the three latitudinal transects analyzed, only the East Transect demonstrated a statistically significant northward migration in PFT distribution. In particular, a Pearson’s Correlation between year and the mean latitude of sites classified as C3-majoirty yielded a slope of 0.1556 and R2 of 0.6410, indicating that the centroid of C3-majorite sites was shifting north by 0.1556 degrees latitude per year. However, this distribution shift appeared to be independent of environmental factors, as nearly all Pearson’s Correlations between changes in PFT distribution and environmental factors were statistically insignificant. These preliminary findings demonstrated that the degree of northward migration of grass PFTs along latitudinal transects varied across geographical regions. Further analysis may be conducted to reveal the strengths of environmental factors for driving PFT distribution shifts and provide projections of PFT migrations under a changing climate.