Lymphomas are present in two varieties-Hodgkin and non-Hodgkin. These cancers pose a significant threat in the domain of oncology due to their varied responses to treatment. Non-Hodgkin’s lymphoma contributes to about 4% of overall cases reported annually. The prevalence of lymphoma is on the increase, particularly among younger individuals, raising concerns about public health. Despite the progress in treatment, the risk of recurrence is between 20% and 30% for Hodgkin lymphoma and much higher for aggressive subtypes of non-Hodgkin’s such as diffuse large B-cell lymphoma. These recurrences with treatment resistance explain poor survival rates for many patients. The diagnosis and management of lymphoma are complicated by the need for invasive biopsies, a very broad spectrum of symptoms, and the constraints imposed by existing imaging and biomarker technologies. Emerging research underlines the critical need for early and accurate detection since delay in diagnosis would significantly affect the success rate of treatment and survival rates. This study focuses on the development of lymphoma detection methods, from conventional through innovative state-of-the-art technologies to recent advancements such as next-generation sequencing and gene expression profiling. Moreover, the paper draws upon the potential of machine learning-based predictive models for the risk stratification of patients and prediction of possible relapse, leading to precise care on an individual basis. This review points out the limitations of the existing diagnostic approaches and urges the integration of novel technologies into clinical practice to increase early detection rates, reduce the impact of relapse, and ultimately improve survival outcomes.