This research investigates the dynamics of workforce management within the gig economy and optimizes gig worker performance plus job satisfaction with predictive modeling and quantitative analysis. The comprehensive dataset from the study employs various machine learning replicas with Linear Regression, Decision Trees, and Random Forests to identify key factors influencing gig work outcomes. The findings demonstrate that advanced copies of the Random Forest Regressor have superior predictive accuracy in understanding gig worker performance and pricing strategies. The research shows the critical roles of worker well-being platform transparency and effective pricing in enhancing gig work efficiency. It is valuable that the study acknowledges limitations such as reliance on the single dataset and model assumptions that affect generalizability. The conclusion suggests that future research should incorporate more diverse datasets, explore advanced modeling procedures, and integrate qualitative methods to be more inclusive of the gig budget; these findings, desirable practical implications for platform operators and policymakers, aim to foster a more sustainable and equitable gig economy.