Complementary and alternative medicine (CAM) has experienced substantial global growth over recent decades, driven by increasing public interest in holistic healthcare, chronic disease management, patient empowerment, preventive medicine, and dissatisfaction with limitations of conventional biomedical approaches in certain chronic conditions. Despite widespread utilisation of interventions such as acupuncture, yoga, meditation, Ayurveda, herbal medicine, Tai Chi, chiropractic treatment, osteopathy, homeopathy, mindfulness-based therapies, naturopathy, and various mind-body interventions, scientific acceptance and integration into mainstream healthcare systems remain constrained by concerns regarding the quality, reproducibility, and methodological rigour of existing evidence. Conventional randomised controlled trial (RCT) methodologies were primarily developed for pharmaceutical interventions involving single active molecules delivered in standardised doses under highly controlled conditions. Many CAM interventions, however, are inherently complex, individualised, practitioner-dependent, multimodal, and context-sensitive, creating substantial methodological challenges when applying conventional biomedical research frameworks. Consequently, robust scientific evaluation of CAM requires methodological adaptation without compromising scientific validity or introducing bias. This article reviews the principles required for designing rigorous CAM trials, including study design, randomisation, control selection, blinding, outcome measures, placebo effects, standardisation, practitioner variability, statistical considerations, implementation science, health economics, systems biology approaches, pragmatic trial designs, real-world evidence generation, and emerging opportunities using artificial intelligence and digital health technologies. Adoption of these approaches may improve the quality of evidence available for CAM interventions and facilitate rational integration of evidence-based complementary therapies into modern healthcare systems.