The increasing exploitation of Artificial Intelligence (AI) enabled systems in critical domains has made trustworthiness concerns a paramount showstopper, requiring verifiable accountability, often by regulation (e.g., the EU AI Act). Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the mechanisms used to achieve this. However, these methods are either expensive or heavily manual and ill-suited for the opaque, "black box" nature of most AI models. An intractable conflict emerges: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect assets being audited-e.g., confidential data and proprietary models-leading to weakened accountability. To address this challenge, this paper introduces ZKMLOps, a novel MLOps verification framework that operationalizes Zero-Knowledge Proofs (ZKPs)-cryptographic protocols allowing a prover to convince a verifier that a statement is true without revealing additional information-within Machine-Learning Operations lifecycles. By integrating ZKPs with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic proof of compliance. We evaluate the framework's practicality through a study of regulatory compliance in financial risk auditing and assess feasibility through an empirical evaluation of top ZKP protocols, analyzing performance trade-offs for ML models of increasing complexity.

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