Congestive Heart Failures (CHFs) are prevalent, expensive, and deadly, causing damage or overload to the pumping power of the heart muscles. These leads to severe medical issues amongst humans and contribute to a greater death risk of numerous diseases at a later stage. We need accurate and less difficult techniques to detect these problems in our world with a growing population which will prevent many diseases and reduce deaths. In this work, we have developed a technique to diagnose CHF using the Electrocardiomatrix (ECM) technique. The 1-D ECG signals are transformed to a colourful 3D matrix to diagnose CHF. The detection of CHF using ECM are then compared with annotated CHF Electrocardiogram (ECG) signals manually. It has been found that ECM is able to detect the affected CHF duration from the ECG signals. Also, the ECM provides the reduction in both false positive and false negative which in turn improves the detection accuracy. The performance of the proposed approach has been tested on BIDMC CHF database. The proposed method achieved an accuracy of 97.6%, sensitivity of 98.0%, specificity of 97.0%, precision of 99.4%, and F1-Score of 98.3% . From this study, it has been revealed that the ECM technique allows the accurate, intuitive, and efficient detection of CHF and using ECM practitioners can diagnose the CHF without sacrificing the accuracy.