Introduction: Geographical access to antenatal intervention is a growing problem in sub-Saharan Africa (SSA). Countries in the regions have for more than 40 years deployed mobile health clinics (MHC) to address that problem, yet, their feasibility, costs, and effectiveness of are poorly known. Methods: A qualitative case study in chapter three using key informant interviews to explore the feasibility of using MHC to deliver maternal health care was conducted. Thematic analysis was done using NVivo software for Mac version 10.5.8. In chapter four exit interviews were conducted to estimate client time and direct costs incurred by clients when they use services. Data were entered in EpiData software version 3.1 and analysed in STATA version 12. In chapter five, a systematic review and meta-analysis of randomised control trial were conducted to synthesis effectiveness data used in the cost-effectiveness analysis (CEA). In chapter six a CEA was conducted from the provider perspective. DALYs lost due to malaria, and severe anaemia was calculated based on recommended guidelines. Cost data were collected from the public district hospital that oversees the MHC. The activity-based costing methodology was adopted to identify measure and value resource used. The Markov decision model was used to compare the cost-effectiveness of using MHC to scale up the provision of IPTp-SP3 and a "wait to treat" strategy. The model was built in Microsoft Excel version 15.37. Probabilistic sensitivity analyses with 10,000 simulations were used to test the robustness of model results. In chapter seven-a, stratified analysis and a net health benefits analysis was conducted to explore the impact of heterogeneity on the estimates when considering parity and HIV status as focus subgroups. Results: Policymakers perceive MHC as an important mode of service delivery to scale up essential interventions in areas that lack static health facilities. Yet, this approach is challenged by scarce resource, poor road infrastructure, and a weak health information system. MHC indicates to have reduced client travel time; however, women incur direct costs due to out of pocket payments driven by out of stock of medicine. The cost of IPTp-SP3 per pregnancy was estimated to be US$ 12.87. The model predicted the total cost to roll out the intervention to 1,000 women to be US$ 20,185 compared to US$ 12,283 if the "wait to treat" strategy is adopted. The strategy was predicted to be more cost-effective than waiting to treat, with an incremental cost-effectiveness ratio (ICER) of US$ 40 per DALY averted. The strategy was also associated with lower costs of the consequences as compared to wait to treat strategy (US$6 086 versus US$12 365). Additionally, more net health benefits (NHB) were predicted when the intervention was provided to low parity and HIV positive women alone. Conclusions: Using MHC to scale up IPTp-SP3 in Tanzania is worth the cost with more NHB predicted for low parity and HIV positive women. Although the presented evidence may help guide decision in scaling up essential health intervention, yet more evidence is needed on the impact of this mode of service delivery on a budget of the ministry of health and social welfare.