The quality of additive manufacturing (AM) built parts is highly correlated to the melt pool characteristics. Hence, melt pool monitoring and control can potentially improve the AM part quality. This paper presents a neighboring-effect modeling method (NBEM) that uses a scan strategy to predict melt pool size. The prediction model can further assist in scan strategy optimization and real-time process control. The structure of the proposed model is formulated based on the physical principles of melt pool formation, while experimental data are used to identify the optimal coefficients. Compared to the traditional power-velocity prediction models, the NBEM model introduces the cumulative neighboring-effect factors as additional input variables. These factors represent the neighborhood impact of scan path on the focal point melt pool formation from time and distance perspective. Two experiments use different scan strategies to collect in situ measurements of the melt pool size for model construction and validation. By introducing the neighboring-effect factors, the global normalized root-mean-square Error (NRMSE) is improved from around 0.10 to 0.08. More importantly, the local NRMSE of irregular melt pool area prediction is improved to around 0.15 for more than 50% improvement. The case studies verify that the proposed method can predict the melt pool variations by seamlessly integrating scan strategy considerations.