The integration of CA and computational intelligence or Artificial Intelligence cast a new dimension to a paradigm in the PH in big data systems. To demonstrate, this research provides an extensive literature review on how big data and cloud analytics empowered by artificial intelligence could be leveraged to transform restaurant inspection information into meaningful knowledge enhancing the community well-being. It employs advanced algorithms in machine learning for predicting the results from inspection and for determining which indicators have the most significant influence on the attainment of compliance by analyzing a huge dataset of restaurant inspection ratings. This involves data cleaning, preprocessing and exploratory data analysis commonly abbreviated as EDA as well as building models on the data. Some of the noteworthy models that were considered under scrutiny include Linear Regression, Random Forest, and GBM. Finally, we show that the two selected algorithms, GBM and Random Forest, perform reasonably well, which is higher than in linear models and shows that it is necessary to use non-linear relationships between characteristics and assessment of inspections. High values of information gain according to the feature importance analysis mean that the most important predictors in predicting the inspection outcomes are geographic characteristics and average scores in previous years. The report talks about the implications of these findings to the regulatory styles that consultant more focus and headout regulations. In their turn, the regulatory bodies may use the AI and cloud computing technologies for more efficient allocation of the resources that are available to them, that is, fund businesses and locations where the calculations show that their impact would be immediately necessary.