The base price of Airbnb properties prediction is still a new area of prediction research, especially with the Extreme Learning Machine (ELM). The previous studies had several suggestions for the advantages of ELM, such as good generalization performance, fast learning speed, and high prediction accuracy. This paper proposes how the ELM approach is used as a prediction model for Air BnB base price. Generally, the steps are setting hidden neuron numbers, randomly assigning input weight and hidden layer biases, calculating the output layer; and the entire learning measure finished through one numerical change without iteration. The performance of the model is estimated utilizing mean squared error, mean absolute percentage error, and root mean squared error. Experiment with Airbnb dataset in London with twentyone features as input generates a faster learning speed and better accuracy than the existing model.