Female sexual dysfunction (FSD) is a common condition that affects women of all ages. It is characterized by a range of symptoms, including decreased libido, difficulty achieving orgasm, and pain during intercourse. One potential cause of FSD is muscular weakness or changes in the core muscles. These muscles play an important role in sexual function, and changes in their strength or activation patterns can lead to FSD. In this proposal, we aim to conduct a comprehensive analysis of changes in core muscles during FSD using machine and deep learning. This study aimed to predict changes in core muscles during female sexual dysfunction using machine and deep learning models. The performance of various models, including Multi-layer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), ElasticNetCV, Random Forest Regressor, SVR, and Bagging Regressor, was evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) score. The results show that CNN achieved the lowest MSE (0.002) and the highest R2 score (0.988). RandomForestRegressor also performed well with an MSE of 0.0021 and an R2 score of 0.9905. The study suggests that machine and deep learning models can be effective in predicting changes in core muscles during female sexual dysfunction.