<p>This paper focuses on developing estimation techniques for generalized partially linear single-index models (GPLSIMs). A notable feature of these models is their systematic component, which adopts a flexible semiparametric structure and incorporates a general link function. We propose pretest and non-penalty shrinkage estimation for GPLSIMs. These methods offer a flexible framework accommodating both linear and nonlinear covariate-response relationships. Our two-stage estimation strategy leverages penalized splines for the single-index function and employs a profile likelihood approach for simultaneous parameter estimation. The core objective is to develop efficient non-penalty shrinkage estimators, particularly when some regression parameters are potentially constrained. This is achieved by combining full and restricted models and incorporating auxiliary information about parameter constraints. We theoretically derive the asymptotic properties, including biases and risks, of these estimators. Their performance is evaluated through Monte Carlo simulations against the unrestricted estimator, and their practical utility is demonstrated via an empirical application.</p>