Abstract: To address the challenges of infrequent detection and prolonged measurement cycles for FeO content in iron ore sinter, this study proposes a soft-sensor model for online estimation of FeO content using routinely monitored parameters from the sintering process as input variables. The model development first employs the Random Forest(RF) algorithm to identify high-impact variables, subsequently constructs a Multilayer Perceptron(MLP) to capture the intricate nonlinear relationships between input variables and FeO content, and finally integrates the Wild Horse Optimizer(WHO) algorithm to optimize MLP hyperparameters, thereby enhancing both model fitting accuracy and generalization capabilities. Validation using real-world production data demonstrates that the proposed model achieves an accuracy of 91.26% within ±0.5% error margin. This framework provides actionable insights for industrial applications, effectively mitigating FeO content fluctuations and advancing operational precision in sintering production, with significant implications for optimizing process stability and elevating metallurgical manufacturing standards.