PINNs offer an efficient alternatives by embidding the govering physics equation directly into the neural networks loss function . PINNS leverage data -driven machine learning tp approximate solutions while incorporating physics constraints, making them suitable for applications in engineering , fluid dynamics and heat transfer. The goal was to solve a 2D transient heat conduction peoblem using PINNs , evulate perforamnce impact of various neural network architectures on accuracy and computational efficiency.