Intraocular pressure (IOP) measurement is one of the basic tests performed in ophthalmology and is known to be an important risk factor for the development and progression of glaucoma. Measurement of IOP is important for assessing response to treatment and monitoring the progression of the disease in glaucoma. In this study, we investigate a method for measuring IOP using the characteristics of vibration propagation generated when the structure is in contact with the eyeball. The response was measured using an accelerometer and a force sensitive resistor to determine the correlation between the IOP. Experiment was performed using ex-vivo porcine eyes. To control the IOP, a needle of the infusion line connected with the water bottle was inserted into the porcine eyes through the limbus. A cross correlation analysis between the accelerometer and the force sensitive resistor was performed to derive a vibration factor that indicate the change in IOP. In order to analyze the degree of influence of biological tissues such as the eyelid, silicon was placed between the structure and the eyeball. The Long Short-Term Memory (LSTM) deep learning algorithm was used to predict IOP based on the vibration factor.