To address the issue of crossinterference from ethanol gas in electrochemical formaldehyde sensors during indoor environmental monitoring, this study developed a formaldehyde detection system using the embedded AI chip RV1126 as the computing platform, with electrochemical formaldehyde and ethanol sensors as sensing components for environmental data acquisition. At the same time, combined with embedded artificial intelligence technology, algorithm compensation is used at the device end to improve the formaldehyde detection instrument’s ability to resist ethanol cross-interference. This study obtained a dataset using grid sampling method, and based on this data, regression models were constructed using linear equation regression, conventional machine learning regression methods, and neural network regression methods. Comparative experiments on root-mean-square error (RMSE) revealed that the linear regression model exhibited the highest prediction error (≈600 μg/m3), conventional machine learning models achieved ≈100 μg/m3 error, while the neural network regression model demonstrated superior accuracy with an MSE of 30 μg/m3. According to the World Health Organization’s Indoor Air Quality Guidelines, which recommend a long-term average formaldehyde concentration threshold of 80 μg/m3, the proposed formaldehyde detector, combined with the neural network model, effectively fulfills detection requirements in environments with ethanol cross-interference, ensuring reliable formaldehyde pollution monitoring.