In edge computing systems, load prediction plays a crucial role in optimizing resource allocation and enhancing system performance. Traditional prediction models tend to perform well when handling loads with clear trends or periodic patterns. However, the dynamic nature of edge environments and the multidimensional complexity of data often challenge these models, leading to decreased accuracy and efficiency in complex scenarios. Recently, advances in patch-based time series prediction methods and frequency domain analysis techniques have significantly improved prediction accuracy in such environments. Against this backdrop, this paper proposes a frequency-domain based model: the Multi-Scale Patch Multilayer Perceptron Network (FMPNET), designed for high-precision load prediction in edge computing. By decomposing time series data into sub-bands of distinct frequencies using the Discrete Wavelet Transform (DWT), the FMPNET model captures both rapid fluctuations and long-term trends. The multi-scale patch mechanism enables short-term patches to identify local fluctuations and transient changes, while long-term patches focus on global trends. This design enhances the model’s capacity to grasp the global structure of the data, preserve fine-grained details, and improve adapt ability to multidimensional load patterns. Extensive experiments on real-world edge computing load datasets demonstrate that FMPNET achieves superior prediction accuracy and efficiency across various scenarios. Compared to benchmark methods, FMPNET not only significantly enhances prediction accuracy but also exhibits strong generalization capability, affirming its effectiveness in dynamic and complex edge environments.