Quantum Machine Learning (QML) has recently been explored as a novel approach to surpass the capabilities of classical methods, although the field remains in its early stages and the outcomes achieved so far are still inconclusive. This talk offers a critical and methodologically grounded perspective on current QML approaches, with particular attention to the fundamental limitations of classical machine learning and the ways in which quantum-enhanced models may be designed to address these challenges. Recent developments in unsupervised and reinforcement learning serve as illustrative examples to examine how quantum formulations, tailored to the structure of specific problems, can yield algorithmic and representational advantages. Methodological aspects such as model design, problem encoding, and hybrid integration are emphasized, along with a discussion of current limitations in quantum computing, including hardware constraints and the lack of mature, task-specific quantum design principles. The talk concludes with reflections on how these insights may inform the development of more robust and effective QML methodologies.