Data-driven Machine Learning (ML) based propagation models are essential for modern wireless network planning and optimization. However, their effectiveness is limited by scarse data conditions. Generative Adversarial Networks (GANs) often considered as a viable approach for data augmentation, struggle in these conditions because they also require large datasets for effective training. To address this challenge, we propose a novel approach that incorporates domain knowledge directly into GAN training. Using an analytical propagation equation based on 3GPP recommendations, we generate pseudo-random data to train a neural network, which then initializes the GAN generator network. This initialization improves the GAN's learning ability in extreme data scarcity. The framework enhances data generation quality by up to 52% and machine learning applicability by 60%, providing a robust solution to the scarse data problem in wireless network modeling with demonstrating the potential of integrating domain knowledge within ML methodologies.