Global trade faces the challenge of compromising product conformance and speeding up the conformity assessment (CA) process. Although the self-declaration CA ensures a fast CA process, it suffers from leakage of impartiality. In the present work, self-declaration CA was validated using machine learning (ML) models. Various ML models have been trained using the market surveillance data, and the highest performance models were selected. Different sources of risk have been investigated, including the risks of using inaccurate ML models, including redundant variables in ML training, leakage of learning in ML models, erroneous ML model predictions, and uncertainty of experimental results. A procedure including recommendations to minimize these risks has been proposed. ML models were applied to assess the electrical performance of air conditioning (AC) units which typically requires 6–8 h in the laboratory. The conformity of AC units could be predicted by ML models with an accuracy exceeding 98%. As a result, the productivity of the laboratory expanded from testing only one AC unit to testing an unlimited number of units per day, saving time, effort, and laboratory resources.