Radio-frequency capacitively coupled plasmas involve strongly coupled particle transport, electron energy transport, sheath dynamics, and self-consistent electric-field evolution. Conventional numerical simulations of the spatiotemporal plasma evolution at different gas pressures and applied radio-frequency voltage amplitudes require the fluid equations coupled with the Poisson equation to be solved separately for each operating condition. Consequently, the total computational cost increases with the number of operating conditions, making large-scale scans of the pressure-voltage parameter space for suitable discharge conditions time-consuming. This work aims to develop a fast surrogate model capable of predicting spatiotemporal plasma characteristics for pressure-voltage combinations not included in training. To this end, a parameterized physics-informed neural network (P-PINN) is constructed for one-dimensional He and Ar discharges between parallel plates separated by 3 cm and driven at 13.56 MHz. The network takes the spatial coordinate, time, gas pressure, and radio-frequency voltage amplitude as inputs and predicts four principal plasma quantities: the electron density, ion density, electric potential, and electron energy density. Its loss function combines sparse fluid-model reference data with the residuals of the electron and ion continuity equations, electron energy equation, Poisson equation, electric field-potential relation, drift-diffusion flux relations, and electrode boundary conditions. Only 1\% of the spatiotemporal reference data from each training condition is used. Separate P-PINN models are trained for the He and Ar discharges and evaluated at pressure-voltage combinations absent from their respective training sets. The predicted spatiotemporal distributions agree closely with the fluid-model reference solutions. The models capture the periodic variations in electric potential, electron density, and electron energy density over a radio-frequency cycle, as well as the main spatial features of the discharge, including the concentration of charged particles in the plasma bulk, the rapid decrease in electron and ion densities near the electrodes, and the potential drops across the sheath regions. They also reproduce the changes in these distributions with gas pressure and radio-frequency voltage amplitude. Compared with neural networks trained using the same sparse data but without the equation and boundary residuals, the P-PINN models generally yield lower relative L_2 errors over the evaluated parameter space, indicating closer agreement with the self-consistent fluid-model solutions. For each gas, ten representative conditions absent from the corresponding training set are evaluated. The mean relative L_2 errors of ion density, electron density, electric potential, and electron energy density are 1.53\%, 1.36\%, 3.44\%, and 1.72\%, respectively, for He, and 1.55\%, 1.21\%, 2.65\%, and 1.60\%, respectively, for Ar. Averaging first over the four quantities and then over the ten conditions gives overall mean errors of 2.01\% for He and 1.75\% for Ar. The electric potential has the largest mean error in both gases, indicating that its reconstruction is the main source of error in the present models. After offline training, the average inference times for one discharge condition are 15.6 ms for He and 14.5 ms for Ar on the computational platform used. For both gases, relatively large errors occur mainly near the boundaries of the sampled parameter domain, particularly under low-pressure and low-voltage conditions. This behavior may be associated with the smaller number of neighboring training conditions near the parameter-space boundaries, which makes accurate inference more challenging. At sufficiently low pressure, the increased electron mean free path also enhances nonlocal electron kinetics, potentially limiting the physical validity of the underlying fluid model and, consequently, that of a surrogate constrained by this model. These results demonstrate that combining sparse fluid-model reference data with the fluid conservation and transport equations, the Poisson equation, and wall boundary conditions enables millisecond-scale prediction of spatiotemporal plasma characteristics. Within the validity range of the underlying fluid model, the proposed method can therefore provide an efficient surrogate for rapid pressure-voltage parameter scans and analysis of discharge operating windows.