Building structures on clayey soil presents unique challenges to geotechnical engineers due to the inherent variability in clayey soil consistency. Understanding engineering properties of clayey soils is essential for accurate geotechnical design and the prevention of potential issues such as settlement and instability. The current study provides crucial insights for geotechnical assessments and engineering solutions in the area, highlighting key soil properties that affect the classification of clayey consistency. Advanced machine learning (ML) models were employed to predict the in situ clay consistency, a vital parameter for evaluating the deformation resistance of clayey soils under structures. The ML predictions are based on nine features representing the physical and mechanical properties of the clay, which are easily determined through laboratory and field evaluations. A dataset comprising 173 samples is compiled, which extracted from Nile Delta in Egypt, incorporating data on the basic properties of the soils to train and test several ML classification algorithms. The classification models, including logistic regression, k-nearest neighbors, support vector machine, random forest, and gradient boosting classifiers, are evaluated using metrics such as accuracy, sensitivity, specificity, and F1-score. The results demonstrate that the gradient boosting classifier model exhibits the highest accuracy in predicting clay class, achieving 97% and 86% accuracy for the training and testing datasets, respectively. These findings offer a valuable framework for efficiently and cost-effectively classifying clays, assisting geotechnical engineers in making informed decisions about foundation design and construction on clayey soils. Additionally, the study establishes equations to predict the undrained shear strength of clayey soil based on its basic properties, providing a practical and accurate method for estimating soil strength characteristics. These contributions enhance the understanding and management of clayey soil behavior in geotechnical engineering, offering essential guidance for foundation design and construction projects in clayey soil regions.