The health and working conditions of employees have become increasingly important issues in modern society. In recent years, there has been a continuous rise in problems related to the deterioration of workers’ alth, which seriously affects their safety and overall quality of life. Although existing research has investigated various factors affecting workers’ health and working conditions, there is still a lack of studies that scientifically analyze and identify key variables from the vast number of factors. This study employs the Lasso (Least Absolute Shrinkage and Selection Operator) technique to mathematically analyze the key variables influencing workers’ health status and satisfaction with their working environment. Lasso is a technique used in machine learning to identify a small number of variables that impact the dependent variable among a large set of variables, thereby reducing model complexity and improving predictive accuracy. The results of the study can be utilized in efficiently improving workers’ health and working environments by focusing on a smaller set of impactful variables.