Determining the service life of building structure is a critical step for the evaluation of maintenance of the building. If the service life of a building is known then proper maintenance and refurbishment steps can be taken. Previous studies focused only on physical obsolescence whereas new concept focused on other new six criteria. The objective of this paper is to use the entire six criteria for the evaluation of obsolescence for the prediction of the age of building a structure using machine learning. A prediction model for predicting age is developed by combining the six obsolescence criteria, absolute weights, diagnostic scores, and machine learning. As obsolescence is predicted using all six criteria, the manual calculation is reduced to provide more accuracy. This prediction model has built using python language, Django as a framework and PyCharm IDE. To make this prediction system more adaptable the website is created and hosted on webhost000.com by combining prediction model and UI of the website which displays the predicted age for respective diagnostic scores. Hence predicting age is useful for taking actions to prevent future degradation.