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Fluoride plays an essential role in terms of the health of human beings. Persistent exposure to fluoride, which is present in drinking water mainly, may result in dental, skeletal, and non-skeletal fluorosis. However, drinking water, being consumed presently, is not sufficient to indicate the degree of exposure to fluoride. The existing literature indicates that nails can be used as indicative (biomarkers) of not only to exposure of fluoride but also the degree of the same. However, because of differential metabolism rate depending on a number of factors like age, gender, nutritional status, water characteristics, etc., exposure to fluoride is not easily detectable in human beings by just testing the fluoride content in nails. Moreover, due to sensitive chemical analysis and lack of facilities, it is difficult to identify the exact concentration of fluoride in nails. The objective of this study is to identify the significant parameters that affect the fluoride content in nail samples. Apart from laboratories test, the application of different artificial intelligence (AI) methods are used for the prediction of fluoride in nails, which will help to identify the degree of fluoride exposure to children, females, and males. The field study covers a collection of 2401 nail samples from eight districts of Rajasthan, India. The samples were taken of different age groups and genders. 1024 water samples were also collected from the same households from where the nails samples were collected. These water and nail samples were tested in laboratories at Birla Institute of Technology and Science, Pilani, India. This data set was used to train, test, and validate the results using AI techniques. The proposed hybrid model (HM) combines Principal Component Analysis (PCA), Firefly Algorithm (FA), and Artificial Neural Network (ANN) to predict fluoride concentration in nails. The model performance was assessed by using different errors and coefficient of relationship. A comparative analysis of the proposed HM was done against ANN and PCA–ANN models. Sensitivity analysis was performed to study the importance of input parameters on the output. Results show that the PCA–FA–ANN–AFpE model having four input parameters (age, fluoride, pH, and electrical conductivity), displayed the best performance with high R2 values of 0.97. The study generates a novel methodology for predicting fluoride concentration in nails, which can be used for the prediction of hotspots in terms of continuous exposure to fluoride. [ABSTRACT FROM AUTHOR]