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Drilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency. [ABSTRACT FROM AUTHOR]