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Owing to the continuous usage of centrifugal pump machines (CPM), health-monitoring systems are important for improving the hydraulic machine industry. While operating the CPM, supervision is carried out by either the operator or the online health-monitoring system. The Health Monitoring System (HMS) helps to understand the conditions of the pump and maintenance prospectus. For breakdown and accidents in the CPM industry, the prime reasons are impeller failure, bearing failure, cavitation, and shaft misalignment; therefore, it is necessary to supervise the parameters to provide a better life to the plant and enhance its efficiency using Data Acquisition (DAQ). The independent features and dependent features are selected and experimentally investigated to examine the characteristics of the CPM plant. The feature selection and extraction process should be compatible with the DAQ system which will help to reduce the curse of data biases and data preparation. Based on that three different DAQ systems based on Arduino, Raspberry Pi, and Dewsoft FFT are used in the experiments and compared based on 5-Point summary, Standard Deviation, cost analysis, flexibility, and maintenance. This paper presents the various techniques and procedures for acquiring the data from CPM using all three DAQ systems and selecting one of them through a statistical approach.