BACKGROUND: Mobile health (mHealth) holds promise for enhancing patient care, yet attrition in its use remains a major barrier. Low retention rates limit its potential impact, while barriers to accessing or adopting mHealth vary across populations and countries. These differences in utilization of mHealth may exacerbate health inequalities, contributing to the digital health divide. OBJECTIVE: We aimed to conduct a systematic review and meta-analysis to investigate the factors associated with inequalities in mHealth utilization across different implementation phases, including access, adoption, adherence, and maintenance. METHODS: This systematic review and meta-analysis analyzed mHealth research from 2000 to May 30, 2024, using databases, including PubMed, Web of Science, MEDLINE, and ProQuest. Eligible studies included smartphones, mHealth apps, wearables, and inequality indicators across 4 mHealth phases: access, adoption, adherence, and maintenance. Excluded studies were nonpeer-reviewed, opinion-based, or not in English. Extracted data included study characteristics, target populations, health outcomes, and inequality factors like age, gender, socioeconomic status, and digital literacy. Factors were categorized using a digital health equity framework (biological, behavioral, sociocultural, digital, health care system, and physical domains). Meta-analyses were performed using a random-effects model for factors reported in at least three studies, with heterogeneity assessed by the I² statistic. RESULTS: Among 1990 studies, 62 studies met the inclusion criteria, and 30 studies underwent meta-analysis. The phases of mHealth utilization were access (n=23, 37%), adoption (n=47, 76%), adherence (n=9, 15%), and maintenance (n=2, 3%). Meta-analysis showed older age was negatively associated with mHealth adoption (odds ratio [OR] 0.47, 95% CI 0.23-0.93), while higher education and income were positively associated in both access and adoption phases. Employment showed significant associations in the access phase (OR 1.49, 95% CI 1.08-2.05), whereas comorbidities (OR 1.39, 95% CI 1.03-1.86) and private insurance (OR 1.63, 95% CI 1.07-2.48) were significantly associated with adoption of mHealth. Women (OR 1.24, 95% CI 1.06-1.45) and physically active individuals (OR 1.64, 95% CI 1.07-2.50) were more likely to adopt mHealth. CONCLUSIONS: The conceptual framework outlined in this study highlights the multifaceted nature of mHealth utilization across all the phases of mHealth engagement. To address these inequalities, tailored and personalized interventions are required at each phase of mHealth utilization. Targeted efforts can enhance digital access for older and low-income adults while promoting engagement through education, insurance support, and healthy behaviors, thereby promoting equitable and effective mHealth use. By recognizing the interconnectedness of these domains, policy makers and health care stakeholders can design interventions that not only address the phase-specific barriers but also bridge broader inequalities in health care access and engagement.