As the number of elderly people suffering from depression increases today, new techniques for active monitoring of depression are in need than ever. Hence this study aimed to propose an approach of identifying depression in the elderly using gait accelerometry and a machine learning algorithm. A total of 45 community-dwelling elderly individuals participated in the study. Twenty-two out of 45 participants were patients with depression and the remaining 23 participants were individuals without depression. The participants completed a 7-meter walking twice at their preferred speeds with an accelerometer on their lower back. The anterior-posterior acceleration signals measured at the lower back while walking were segmented into acceleration falling and rising phases. Then eight descriptive statistical and six morphological parameters were extracted from each phase. The extracted parameters were ordered chronologically and used as a gait sequence feature. The 4-fold cross-validation of the bidirectional long short-term memory network-based classifiers that used the gait sequence feature as input showed an average accuracy of 0.956 in classifying the elderly with depression and those without depression. The study is expected to serve as a milestone exploring the use of gait accelerometry in assessing various health conditions in the future. Clinical Relevance- The findings of this study will pave a new way for self-monitoring of health conditions in the daily life of individuals, which can open the door for earlier recognition of health risks and more timely treatment.