This paper presents a robust and efficient quantum property learning protocol called robust shallow shadows, which improves upon traditional randomized measurements for quantum state characterization. The protocol uses Bayesian inference to learn and mitigate experimental noise, enabling accurate predictions of quantum state properties such as fidelity, entanglement entropy, and expectation values. Unlike traditional single-qubit measurements, which suffer from high sample complexity for nonlocal observables, the robust shallow shadows protocol maintains experimental feasibility while achieving better sample complexity for a broader class of observables, including high-weight Pauli observables and global low-rank properties. The protocol is tested on a superconducting quantum processor with 18 qubits, where it demonstrates improved performance over traditional methods, particularly in the presence of noise. Theoretical analysis shows that the optimal circuit depth for the protocol depends on the noise strength, with deeper circuits being more effective for low-noise scenarios. The protocol also enables efficient postprocessing using tensor networks, which allows for accurate estimation of both linear and nonlinear observables. The results demonstrate that the robust shallow shadows protocol is scalable, robust, and sample-efficient for characterizing quantum states on current quantum computing platforms. The protocol's effectiveness is validated through experiments on various quantum states, including the cluster state and the AKLT resource state, showing accurate predictions of observables such as fidelity, Pauli observables, and subsystem purity. The work highlights the potential of robust shallow shadows for real-world quantum computing applications, including quantum machine learning, quantum chemistry, and quantum many-body physics.This paper presents a robust and efficient quantum property learning protocol called robust shallow shadows, which improves upon traditional randomized measurements for quantum state characterization. The protocol uses Bayesian inference to learn and mitigate experimental noise, enabling accurate predictions of quantum state properties such as fidelity, entanglement entropy, and expectation values. Unlike traditional single-qubit measurements, which suffer from high sample complexity for nonlocal observables, the robust shallow shadows protocol maintains experimental feasibility while achieving better sample complexity for a broader class of observables, including high-weight Pauli observables and global low-rank properties. The protocol is tested on a superconducting quantum processor with 18 qubits, where it demonstrates improved performance over traditional methods, particularly in the presence of noise. Theoretical analysis shows that the optimal circuit depth for the protocol depends on the noise strength, with deeper circuits being more effective for low-noise scenarios. The protocol also enables efficient postprocessing using tensor networks, which allows for accurate estimation of both linear and nonlinear observables. The results demonstrate that the robust shallow shadows protocol is scalable, robust, and sample-efficient for characterizing quantum states on current quantum computing platforms. The protocol's effectiveness is validated through experiments on various quantum states, including the cluster state and the AKLT resource state, showing accurate predictions of observables such as fidelity, Pauli observables, and subsystem purity. The work highlights the potential of robust shallow shadows for real-world quantum computing applications, including quantum machine learning, quantum chemistry, and quantum many-body physics.