Applying a neural network model, this study investigates the intricate relationships between health status, socioeconomic factors, and employment outcomes. The research explores how individual health conditions and variables such as education, age, work experience, skills, and geographic location influence employment probabilities by analyzing data from various demographic and health surveys. The neural network model, comprising an input layer for key predictors, multiple hidden layers for non-linear processing, and an output layer for classifying employment status, aims to reveal complex dynamics that traditional statistical methods might overlook. The results, processed through a softmax function, are expected to provide valuable insights into the interplay between health and employment, informing policymakers, healthcare providers, and employers. This approach contributes to understanding economic inequality, social mobility, and public health, offering a data-driven foundation for more informed decision-making in workforce development and social welfare strategies.