In this study, we evaluated the effectiveness of various deep learning parameters in detecting audio deepfakes using convolutional neural network (CNN) architectures. Through a series of experiments and comparative analyses, we developed four distinct models, each with different activation functions, optimizers, and learning rates. These models were meticulously trained and evaluated using a comprehensive dataset containing both fake and genuine audio samples. The results indicate that Model One achieved an exceptional accuracy of 97.8%, primarily due to the effective use of ReLU activation and the Adam optimizer. Additionally, Model Four showed significant improvement, attaining a validation accuracy of 96% by employing advanced activation functions and the Adagrad optimizer. In contrast, Model Two, which used a sigmoid activation function in its fully connected layer and the RMSprop optimizer, and Model Three, which utilized the hyperbolic tangent activation function along with the stochastic gradient descent optimizer, demonstrated moderate accuracies.