With the escalating digitization of the modern era and the infusion of technology into almost every aspect of human lives, biometric authentication systems have become the need of the hour to control the identity thefts. They are the automated systems that divulge individuals identify based on their unique biological or behavioral personality traits like fingerprints, face, iris etc. This article presents personal recognition using Foot biometrics by following machine learning approach. The implementation of three supervised machine learning methods namely, Regression, Classification and ANN (artificial neural network) has been done. The proposed method works in two stages: Geometric Feature Extraction and implementation of Machine Learning algorithms. Firstly, handcrafted foot features are extracted using geometrical methods which are instilled as input to three supervised machine learning algorithms to predict the identity of user. Experimental results reveal that the weighted KNN model is the most performant method among all the implemented classifiers with 99.5% validation accuracy and the overall training time of 0.29886 seconds. While other two utilized and tested supervised machine learning methods, also achieved a reasonable accuracy of 99.15% by Squared Exponential GPR model (Regression) and 97.47% by ANN (Feed Forward Neural Network with Back Propagation).