The global diffusion of algorithmic management—where data-driven systems allocate work, evaluate performance, and enforce organizational rules—has transformed labor relations across diverse economic and cultural contexts. From digital labor platforms and multinational supply chains to service and manufacturing sectors in both developed and developing economies, algorithmic systems increasingly mediate the relationship between workers and organizations. While these technologies promise efficiency, objectivity, and scalability, their implications for employee trust remain underexplored, particularly from a global perspective. This study investigates how employees across algorithmically managed work environments perceive control, fairness, and autonomy, and how these perceptions shape trust in organizational systems operating under algorithmic governance. Grounded in organizational trust theory and justice-based frameworks, the study adopts a mixed-methods research design combining survey data with semi-structured interviews conducted among employees working under algorithmic oversight in multiple organizational settings. Quantitative findings indicate that perceived procedural fairness, transparency of algorithmic decision-making, and opportunities for autonomy significantly enhance employee trust, regardless of sector or national context. In contrast, opaque algorithms, intensive digital surveillance, and limited avenues for worker voice consistently undermine trust. Qualitative evidence reveals that these challenges are particularly pronounced in contexts characterized by labor precarity, power asymmetries, and weak institutional protections—conditions prevalent in many developing and transitional economies. The findings suggest that algorithmic management often reproduces existing global inequalities by amplifying managerial control while reducing employee agency, especially where workers lack bargaining power or access to explanations and appeals. At the same time, when organizations integrate human oversight, contextual sensitivity, and transparent communication into algorithmic systems, employees are more likely to perceive such technologies as legitimate and trustworthy. This study contributes to the growing global literature on algorithmic management by centering employee perceptions across varied labor contexts and highlighting trust as a critical mediator between technology and organizational outcomes. The study offers practical implications for policymakers and organizations worldwide, emphasizing the need for human-centered, context-aware algorithmic governance to foster fair, autonomous, and trust-based workplaces in an increasingly digitized global economy.