Back Propagation (BP) neural network has been widely used to establish monitoring models in analyzing dam deformation data. Nevertheless, when optimizing the BP neural network parameters, Ant Colony Optimization (ACO) algorithm converges slowly in the beginning due to completely random search. In the present study, a dam deformation monitoring model combining genetic ACO, BP, and Markov Chain (MC) is built to tackle this problem. First of all, Genetic Algorithm (GA) which has remarkable ability of global search is introduced to help guide the initial distribution of pheromone, and the optimal solution is obtained by the positive feedback of ACO to train BP neural network to get the predicted values of dam deformation. Since the advantages of the two algorithms are complementary, this improvement greatly reduces the time taken in the initial stage of optimization and avoids falling into the local optimum. Furthermore, to improve the prediction accuracy, MC is employed to correct residual errors of the prediction results. Engineering application case manifests that the model is of good ability of fitting and prediction with fast searching speed in parameter optimization.