Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for band selection remain underutilized. To address this gap, this study, conducted in Gejiu, Yunnan Province, China, proposes a multi-algorithm band selection method to enable the rapid prediction of lead (Pb) contamination levels in soil. To construct a preliminary Pb content prediction model, the initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). The results indicated that WOA achieved the highest modeling accuracy. Building on this, a combined WOA-based band selection method was developed, including combinations such as WOA-CARS, WOA-GA, WOA-MI, and WOA-SPA, with multi-level band optimization further refined by MI (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). The results showed that the WOA-GA-MI model exhibited optimal performance, achieving an average R