This paper presents the development of a Time-limited Occupational Therapy (OT) Model designed to address the significant challenge of organizing the OT process within the constraints of a time-limited protocol while maintaining a client-centered approach and promoting systematic interdisciplinary care. Rooted in the authors’ clinical experiences and supported by existing literature, this challenge is compounded by multifactorial and contextual barriers within the Philippine healthcare system, including financial and resource limitations, institutional policies, and the uneven distribution of occupational therapists. Integrating the Dose-Effect (DE) and Good-Enough Level (GEL) models, this framework balances efficient treatment delivery with flexibility to meet client-specific goals. The DE Model emphasizes early improvement, while the GEL Model allows therapy to adapt dynamically to client progress. Key safeguards, such as Goal Attainment Scaling (GAS) and PostIntervention Review and Feedback based on the 5 A’s Model (Assess, Advise, Agree, Assist, Arrange), enable precise tracking of incremental progress and foster self-management through actionable feedback and goal-setting. The model comprises three main phases: evaluation, intervention, and re-evaluation, each structured by standardized measures, collaborative goal setting, and personalized intervention strategies. Interdisciplinary collaboration, informed by frameworks like the Occupational Therapy Practice Framework (OTPF-4), further enhances its adaptability across diverse clinical contexts. Demonstrating its practical application, a sample case of an 80-year-old inpatient highlights effective outcome measures and transition planning. While preliminary, this model provides a structured yet adaptable framework for delivering high-quality, outcome-driven care despite systemic constraints. Future research should prioritize empirical validation to refine the model and evaluate its long-term effectiveness in addressing the complexities of therapy delivery under resource-constrained conditions.