We present novel algorithmic techniques to efficiently verify the Kruskal rank of matrices that arise in sparse linear regression, tensor decomposition, and latent variable models. Our unified framework combines randomized hashing techniques with dynamic programming strategies, and is applicable in various settings, including binary fields, general finite fields, and integer matrices. In particular, our algorithms achieve a runtime of $\mathcal{O}\left(dk \cdot \left(nM\right)^{\lceil k / 2 \rceil}\right)$ while ensuring high-probability correctness. Our contributions include: A unified framework for verifying Kruskal rank across different algebraic settings; Rigorous runtime and high-probability guarantees that nearly match known lower bounds; Practical implications for identifiability in tensor decompositions and deep learning, particularly for the estimation of noise transition matrices.