Unemployment is a complex and chronicle issue that has significant consequences on the economic growth, social justice, and the effectiveness of the policy provisions. The paper summarizes a detailed, statistical-based study of employment patterns and unemployment through machine learning-based Python skills together with statistical tools. The analysis rides on publicly released data sets such as labor force surveys, state economic indicators, and time-series unemployment data to unravel the direction in time, geography and industries in joblessness. The critical social economic variables triggering the analyses are spotted through stringent filtering of data, exploratory data analysis, and features selection through GDP growth, inflation, and development of the industrial sectors. Several predictive models such as Linear Regression, Random Forest, ARIMA and Long Short-Term Memory (LSTM) networks are applied and compared in terms of R 2 measurements, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The findings show that deep learning and ensemble types differ in that they are more accurate than the traditional methods in predicting non-linear and long tendencies. The insights and visualizations that will be created based on the current analysis will be used to inform the creation of policies based on data, specific employment-based programs and planning new workforce. The research highlights a possibility of hybrid machine learning framework to reinforce the forecasts of the labor market and presents a scalable approach linking traditional economic understanding and the latest AI applications to enhance unemployment mitigation tools.