Design-Technology Co-Optimization (DTCO) can be significantly accelerated by employing Neural Compact Models (NCMs). However, the effective deployment of NCMs requires a substantial amount of training data for accurate device modeling. This paper introduces an Active Learning (AL) framework designed to enhance the efficiency of both device modeling and process optimization, particularly addressing the challenges of time-intensive Technology Computer-Aided Design (TCAD) simulations. The framework employs a ranking algorithm that assesses metrics such as the expected variance from the neural tangent kernel (NTK), TCAD simulation time, and the complexity of I-V curves. This strategy considerably reduces the number of required simulations while maintaining high accuracy. Demonstrating the effectiveness of our AL framework, we achieved a 28.5% improvement in MSE within a 30-minute time budget for device modeling, and an 86.7% reduction in the data points required for process optimization of a 51-stage ring oscillator (RO). These results offer a streamlined, adaptable solution for rapid device modeling and process optimization in various DTCO applications.