<p>Magnetic nanomaterials (MNMs) have emerged as a transformative platform in nanomedicine, enabling integrated theranostic applications that combine medical imaging and hyperthermia therapy. Their unique properties, such as superparamagnetism and biocompatibility, are crucial for enhancing diagnostic accuracy and targeted cancer treatment. However, the field faces significant challenges, including the difficulty of optimizing multiple interdependent parameters to simultaneously maximize imaging contrast and heating efficiency, alongside issues of synthesis reproducibility, biocompatibility, and clinical translation. This paper addresses these gaps by providing a comprehensive analysis of the strategic optimization of MNMs through a critical review of recent advances. We demonstrate that advanced architectures—such as core–shell structures, doping, and defect engineering—can dramatically enhance performance, yielding specific absorption rate (SAR) values exceeding 950 W/g for hyperthermia and transverse relaxivities (<i>r</i>₂) greater than 400&#xa0;mM⁻¹&#xa0;s⁻¹ for MRI contrast, substantially outperforming conventional spherical iron oxide nanoparticles. Furthermore, we highlight that artificial intelligence (AI) and machine learning are pivotal in accelerating the data-driven design of high-performance MNMs, predicting optimal properties, and personalizing treatment parameters, reducing development cycles by up to 40%. The implications of this paper are substantial, offering a coherent theoretical framework and a strategic roadmap for developing next-generation theranostic agents. By synthesizing current insights and proposing actionable recommendations, this work bridges critical gaps between laboratory innovation and clinical application, paving the way for more effective, personalized cancer therapies and advancing the field of precision oncology.</p> Graphical abstract <p></p>