The adoption of predictive maintenance, while promising, poses several significant challenges for small and medium-sized industries (SMIs). These challenges arise primarily because SMIs often lack the financial resources, infrastructure, and advanced technical expertise that larger companies typically possess. As a result, many of these industries face substantial barriers when attempting to integrate cutting-edge technologies into their operations. This paper explores and compares three widely recognized predictive maintenance methods: the Markov process, reliability analysis, and neural networks. Each method is distinct, offering unique approaches to predicting equipment failures and improving maintenance strategies. The Markov process relies on probabilistic state transitions, reliability analysis on historical performance data, and neural networks on complex data-driven models. By evaluating their performance in terms of predictive accuracy, cost efficiency, computational complexity, and ease of implementation, this study specifically addresses their applicability within the context of Moroccan SMIs. Ultimately, the findings aim to guide these industries toward adopting the most practical and effective predictive maintenance strategies suited to their operational constraints.