Movable bridge decks experience critical expansion in summer, leading to uncertainty and unpredictability in its availability doe to improper docking and safety hazard. If the bridges are not cooled soon, the inertia of expansion stays, causing prolongation of availability problems. Structural health monitoring of such bridges with a predictive maintenance approach can help plan remedial measures on the exact day and time. For the efficient design of such a structural health monitoring system, a combination of sensor system data and weather API data is tested. A machine learning approach of Gaussian process regression which can give the results on the prediction of critical expansion of bridge deck has been evaluated in this research project. Finally, a check on the transferability of the prediction model is conducted by application on another bridge data and its performance is discussed. Scenario analysis with savings in cost per scenario is also conducted with varying levels of potential unavailability penalty costs, which could be levied on an asset manager of a bridge if the prediction models of sensor system data set or/and weather API data set gives incorrect estimation. Such an analysis is done to justify the use of the prediction models in assumed scenarios to predict expansion of movable bridge deck