NABLA-SciML is a unified framework for efficient and reproducible implementations of scientific machine learning methods including Physics-Informed Neural Networks (PINNs), DeepONets, and Kolmogorov-Arnold Networks (KANs). The repository contains tutorial modules demonstrating these architectures using both PyTorch and JAX, alongside research modules for Residual-Based Attention mechanisms, variational frameworks, and applications to complex physical systems such as turbulent flows and cerebrospinal fluid dynamics.