People with disabilities are people with abnormalities or physical disabilities, intellectually or mentally. Usually, persons with disabilities will receive a pension or compensation for living costs from the government. However, the transportation cost is another concern for the handicapped living in a developing country because distinctive disability types may fluctuate the costs. This paper will show the comparison of artificial intelligence techniques, Artificial Neural Network, Decision Tree Classifier, LR, LDA, k-Nearest Neighbor, CART, and Naïve Bayes algorithms to foresight the transportation cost for persons with disabilities, from their home to the government office center, to help the government allocate subsidiary funds to aid persons with disabilities. The details of the cleaning dataset and some better accuracy result configuration will explain in detail. The models can be utilized to predict the expected transportation cost from a bird’s-eye view of the government. It also could be adopted for public transport outlining and other amenities for persons with disabilities in Songkhla Province, Thailand.