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Currently, there are many enterprises involved in extracting and processing of primary raw materials. The danger of working in this industry consists in the formation of cracks in rocks of the pit side slopes, which can lead to destruction. This article discusses the existing systems for monitoring the pit collapse prevention. The most promising is the use of systems with fiber-optic sensors. However, use of these systems is associated with some difficulties due to high costs, low noise immunity, and in some cases, the requirement for additional equipment to improve the reliability of measurements. A completely new method of processing the data from a fiber-optic sensor that simplifies the design and reduces the cost of the device is proposed considering the experience of previous developments. The system uses artificial intelligence, which improves the data processing. The theoretical part is dedicated to the development of foundations, and the analysis of the nonlinear properties of the physical and mathematical model of optical processes associated with the propagation of an electromagnetic wave in a fiber-optic material was developed. The results of experimental and theoretical applied research, which are important for the development of fiber-optic systems for monitoring the pit collapse prevention, are presented. The dependences of optical losses and the number of pixels on the dis-placement were obtained. The accuracy of the method corresponds to the accuracy of the device by which it is calibrated and is 0.001 mm. The developed hardware-software complex is able to track the rate of changing the derivative of the light wave intensity in time, as well as changing the shape of the spot and transition of pixels from white to black. [ABSTRACT FROM AUTHOR]