Human Action Recognition (HAR) has advanced significantly with the integration of deep learning and machine learning techniques, enabling more accurate and efficient recognition systems. This paper explores three approaches using different data modalities for action recognition on two widely used datasets, UCF50 and UTD-MHAD. Two distinct hybrid CNN-LSTM models were implemented for the UCF50. In these models, Convolutional Neural Networks (CNNs) are utilized to extract spatial features from RGB video frames, while Long Short-Term Memory (LSTM) is employed to simulate the temporal dependencies of human actions. Machine learning models are proposed in conjunction with the extraction of a collection of novel spatio-temporal features from skeleton data using the UTD-MHAD dataset. These models incorporate algorithms such as Support Vector Machines (SVM) and Random Forest, as well as Artificial Neural Networks (ANN). Additionally, this work proposes unique image representation of skeleton data and CNNs are employed to classify skeleton spatio-temporal images generated from UTD-MHAD providing enhanced recognition. The models exhibit a high level of accuracy, with 94% for a subset of actions from UCF50 and 80% for UTD-MHAD. This demonstrates the robustness of our approach in managing variability across various individuals.