Moreover, optimization and learning algorithms have gained attention in SMC research due to improving controller performance and adaptability under various process operating conditions [46; 47; 48; 49]. Furthermore, adaptive techniques, which allow controllers to adjust to system uncertainties and variations, and predictive strategies, have been integrated into SMC frameworks [50; 51; 52; 53; 54; 55]. Moreover, incorporating observers has enhanced SMC design and performance, facilitating state estimation and feedback control in real-time applications [56; 57; 58]. Figure 2 illustrates the fundamental structure of SMC design in a model-free framework. Unlike traditional SMC approaches, this method does not rely on a mathematical model of the system. Instead, it utilizes input-output data from the process and the reference signal to formulate the control law. The theory and applications of SMC have been documented in recent surveys. [PERSON] et al. [10] focused on terminal SMC (TSMC) combined with intelligent and adaptive schemes, addressing some examples of SMC applications. [PERSON] et al. [59] contributed a comprehensive review article, essentially serving as a tutorial for the development of sliding mode predictive control, a hybrid control strategy taking advantage of the strengths of both MPC and SMC. Furthermore, [PERSON] et al. [60] explored hybrid control systems that incorporate fuzzy logic systems, computational intelligence algorithms, and SMC. On the other hand, [PERSON] et al. [61] provided an overview of SMC based on an adaptive neuro-fuzzy inference system (ANFIS) for nonlinear systems and that it helps to limit high-frequency chattering. [PERSON] et al. [62] presented a complete review of the theory and applications of TSMC, examining the technical aspects and the challenges and future opportunities of TSMC in the context of technologies such as cyber-physical, artificial intelligence, and networking systems. There are SMC review articles that examine specific applications or processes. Some of these articles explore using SMC in networked control systems, highlighting SMC schemes designed to handle challenges such as time delays, packet losses, quantization, uncertainty, and disturbances [63; 64]. In another line, it is possible to find works that address the application of sliding mode surfaces in the design of observers and controllers for synchronous motors [65; 66; 67; 68]. Another area of interest is using SMC in treating photovoltaic systems and power converters [69; 70; 71; 72]. [PERSON] et al. [73] presented the characteristics of variable  
Figure 2: Model-free SMC design. structure control with sliding modes to design robust consensus controllers for multi-agent systems using nonsmooth analysis tools. [PERSON] et al. [74] described the advantages and disadvantages of SMC and discussed the chattering problem while reviewing SMC in the field of fault-tolerant control. [PERSON] et al. [75] presented the advances in research and development of switched systems, combining them with SMC, which allows researchers to work with complex and changing systems, improving their efficiency and stability. [PERSON] et al. [76] reviewed advances in SMC in autonomous underwater vehicles. To the best of our knowledge, there is currently no comprehensive review specifically addressing data-driven approaches within the context of sliding mode control (SMC). This work aims to fill this gap by providing an extensive overview of trends and developments in data-driven sliding mode control (DDSMC) techniques. The key contributions of this research are as follows:  
1. A detailed bibliometric analysis is carried out, highlighting publication trends and introducing a cumulative production model that predicts both the deceleration point in scientific output and the stabilization point for research in DDSMC. This offers valuable foresight into the evolution of this research area. 2.