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Millions of people suffer from heart failure worldwide. The need for heart donations started the development of mechanical circulatory support systems. A suction phenomenon can occur in the artificial heart when not enough blood is available. Due to occlusion, suction in the artificial heart can cause the arteries to collapse and have fatal consequences. This thesis is in collaboration with Scandinavian Real Heart AB and follows the implementation of a preemptive atrial suction detection algorithm for the total artificial heart (TAH) developed by Realheart. The main limitation is the number of sensors available to collect data from, restricted to a pressure and current sensor. The data used in the thesis is collected on a mock loop that simulates the pressures in the human body. The implementation follows an iterative process where different Artificial Intelligence algorithms are tested and evaluated. The final algorithm uses a recurrent neural network (RNN) for classification and is evaluated based on the accuracy and the number of seconds before suction occurs. The results show that the RNN can preemptively classify the data one second before it occurs. The algorithm assumes suction to happen one second before it occurs, preemptively detecting suction. The results from this thesis enable a continuation that can improve the development of TAHs. Future work includes an addition of features for a more accurate and robust algorithm, a more diverse dataset, an improved labelling process and the addition of a time axis to the RNN to improve the time before suction is detected.