This research investigates the effectiveness of various vibration data acquisition techniques coupled with different machine learning models for detecting anomalies and classifying them. To this end, synthetic vibration data was generated for techniques such as eddy current proximity transducers (ECPT), accelerometer sensor, blade tip timing, laser doppler vibrometer (LDV), and strain gauge. Afterward, the data was pre-processed and used to train gradient boosting machine, support vector machine, and random forest models. Performance evaluation metrics, including accuracy, recall, F1-score, receiver operating characteristic, and area Under curve were employed to assess the models, revealing varying degrees of success across combining techniques and models. Notable achievements were observed for the random forest model coupled with the eddy current proximity transducers technique, underscoring the significance of informed technical selection and model optimization in enhancing vibration anomaly detection systems in combined cycle Power plants. The results showed that the LDV technique has a significant increase in accuracy from about 0.49 to approximately 0.52, while the ECPT technique has improved from about 0.9 to close 1.0. These advances highlight the growing accuracy of the methods and enable the development of more efficient and reliable learning machines.