As people’s awareness of the environment gradually increases and their requirements for the comfort of living space become higher, landscape design has also ushered in a golden period of development. With the increasing investment in landscape construction in urban development, the area of park green space has been increasing. A park is a place that provides recreation and relaxation for the public. However, the mere pursuit of landscape quality and artistic effects without effective cost control will eventually lead to a rise in construction costs. Therefore, this study explores the main influencing factors that lead to high park landscape costs by analyzing the current development of park landscape design. Based on the comprehensive analysis, a park landscape cost prediction model based on recurrent neural networks is proposed in order to better control the construction costs of park landscapes. This study applies advanced deep learning technology to the project management of park landscapes, which effectively improves the accuracy of cost prediction. In addition, an artificial bee colony algorithm is introduced to update the weights of the recurrent neural network, resulting in a globally optimal ABC-RNN prediction model. The experimental results show that the proposed ABC-RNN prediction model has higher prediction accuracy and stability than the commonly used prediction models.