DC microgrid has relatively more advantages of power quality, not requirement of reactive power, higher operational efficiency compare to AC microgrid. DC microgrid can facilitate effective integration of distributed clean energy resources and efficient solution for providing electricity to remote areas (e.g. North Eastern States of India). Recently, India has commissioned small hydro, solar PV and battery storage integrated DC microgrids (MGs) to meet the locally increasing load demand of northeastern states. The sudden change in solar insolation during the load power dynamics can cause unbalanced power flow in such isolated MGs. Due to the slow response time of small hydro power plant (SHPP) and limited output power of battery storage with fixed C-rate, the unbalanced power flow, during load power dynamics, cannot be compensated. The unbalanced power flow may lead to unsustainable voltage control at the DC bus of MG. To prevent this, MG follows load shedding. But, load shedding reduces the reliability of MG. To achieve the sustainable voltage control of DC MG, a smart adaptive energy management strategy (AEMS) is proposed in this research work. The novel aspect of proposed AEMS is that it operates the SHPP despite its slow response time by estimating the load power dynamics on the iterative basis. The deep charging/deep discharging scenario of battery storage due to mismatch between the total generation with estimated load and the actual load is taken care by the adjustable energy controller of proposed AEMS. To justify the potential contributions of proposed AEMS, it is assessed against various dynamic test load cases. Based on the assessment of obtained results against various test load cases, in this work, a comparative analysis is carried out between the proposed AEMS and the existing control strategies in the literature. The comparative analysis reveals that with the proposed AEMS, voltage sustainability of MG is improved by 22.7% and the utilization factor of SHPP is enhanced by 55.27% with 98.17% reduction in current stress levels of battery storage system. Finally, the proposed AEMS is evaluated in MATLAB/Simulink as well as validated through OPAL-RT real time simulator.