Obesity is another common health issue that doesn’t care about a person’s gender and has gained attention in recent years. Future prevalence estimations made by the WHO indicate that threshold levels of obesity and overweight will impact more than 50% of the world’s population by 2030. This increasing health issue illustrates the importance of preventive measures and the timely discovery of any possible causes. The present instruments are restricted mainly to simple BMI computation and exclude vital factors such as family history, exercise regimen, genealogy, and other related factors. This work introduces an original computational intelligence model for integrated assessment and prediction of obesity threats. Our method utilizes high-level algorithms, namely SVM, RF, and XGB algorithms, that have been enriched with precise pre-processing strategies such as removing outliers, handling missing and duplicate data, and scaling. In addition, measures such as univariate and multivariate analyses were conducted on the different variables to understand the obesity factors better. Random searches for the hyperparameters further enhanced the capability of the proposed method in optimizing the models with substantial improvements in the spheres of predictive precision. The tunned XGB model achieved the highest accuracy of 97.61%. This research is helpful for the fight against obesity as it employs computerized analyses to shed light on the diverse aspects of obesity and enhance approaches to its early diagnosis.