The invention discloses an electricity market monthly electricity utilization prediction method. The prediction method comprises the following steps of step 1, setting an electricity utilization prediction model, defining a predicted state space model, performing complementary prediction by using a random forest model, loading randomForest, rpart software packages into R software, and leading in an explaining variable and an explained variable; and step 2, determining a prediction model input amount, establishing an air temperature aggregative indicator, adjusting a mobile holiday effect through an effective working day method, predicating a leading indicator, and determining a leading period through model measurement and calculation and coefficient calculation. According to the electricity market monthly electricity utilization prediction method, after the air temperature aggregative indicator, the leading indicator, the business expanding prediction indicator and the like are determined, a state space vector model and a random forest machine study model are combined for performing electricity consumption prediction, so that the prediction method is more accurate and effective.