This article introduces a ridge estimator within the Zero-Inflated Probit Bell (ZIPBell) regression model, developed specifically to handle count data characterized by excess zeros and multicollinearity among predictor variables. By incorporating ridge penalization into the ZIPBell framework, we provide a methodology that stabilizes parameter estimates by reducing variance and mitigating multicollinearity effects without excluding correlated predictors. A numerical study and an empirical application illustrate the robustness of this approach across varying levels of multicollinearity and data sparsity, presenting a reliable tool for analyzing complex count data with structural zeros and correlated predictors.