Deepfakes have become an emergent threat to digital security, media credibility, and individual privacy; they refer to advanced AI techniques that are utilized to change or create fabricated audio-visual contents. The scope of this work is a constructive appraisal of several multimodal deepfake detection algorithms with emphasis on the use of Random Forest, Decision Tree, ResNet, and K-Nearest Neighbor (KNN) architectures. The evaluation entails studying the deepfake identification performance of these models as focused on manipulated images possibly others among many other types of media to identify their advantages and drawbacks. Aside from that of learning about the performances of these conventional machine learning and deep learning techniques, this method also bring forward a new hybrid model of CNN-LSTM. This hybrid approach is effective in overcoming challenges associated with detecting minute manipulations of images since the model can analyze the image in both space and time. The objective of the usability study is to compare accuracy, precision, recall, and general operating speeds for each of the models examined. Thus, we aim at pointing out the best algorithms available for practical use in detecting deepfakes. This research furthers measures aimed at dealing with the problems related to the abuse of AI-created content by proposing an advanced solution in the form of a model meant to enhance the applicability of detection techniques in different available modalities.