Malaria continues to be endemic across Cameroon, causing thousands of deaths every year. With the ever-increasing mosquito resistance to insecticides, it becomes more important to consider the addition of other control strategies to help prevent the disease. Larval source management (LSM), in particular, continues to show significant decreases in transmission while still being low-cost, but it has yet to see wide-scale implementation in Cameroon. The proposed Seek and Destroy (S&D) program is an LSM strategy that combats malaria through the prediction and identification of malaria mosquito habitats followed by destruction and continuous suppression of said habitat utilizing real-time artificial intelligence, [AI] machine learning, [ML], unmanned aerial vehicle [UAV], or intelligent, smartphone application [app] technology, Python, ArcGIS Pro, and training of local abatement vector control officers. Once these habitats are identified, vector control officers can continuously suppress them, leading to significant decreases in vector density. These habitats can range from commercial roadside ditches (~1 m × 0.5 m) to agroecosystem pastureland swamps (~10 m x 10 m), which can be identified geolocated and treated with our proposed real-time, S&D, Artificial Intelligent [AI]-ML, pipeline workflow. We have built a customized, intelligent iOS app that allows for a drone to be flown over areas of suspected habitats with a built-in multispectral [Red, Green and Blue (RGB)] camera that can generate capture point signatures that can be interpolated using gridded, satellite visible and near-infra red [NIR] reflectance emissivity data [e.g., WorldView-2 satellite data which provides commercially available imagery of 0.46 m(m) resolution, image wavebands]. By so doing, real-time unmanned aerial vehicle [UAV] real time identification of potential georeferenceable, [GPS indexable], hyper/hypoendemic, aggregation/non-aggregation-oriented [i.e., hot/ cold spots] of sentinel site larval habitat, breeding sites can be optimally conducted, and field data recorded to make the AI–ML algorithm more accurate continuously. The S&D real time program was executed during a baseline collection in Cameroon to display the capability of malarial reduction in a hyperendemic malarious location. Baseline collection was carried out in two vegetable farming areas: the intervention site and a control site. The program's efficiency was measured based on weekly entomological surveillance through Human landing catches (HLC) and Pyrethrum spray catches (PSC) and biting rates. After four weeks post-intervention, there was a significant decline in indoor resting adult anopheline mosquitos and a steady decline in biting rates at the intervention site. The control site showed the same number of indoor resting adult anopheline mosquitoes and a similar biting rate. These results indicate the immense potential for malarial suppression by employing the S&D, real time, AI-ML assisted program alongside ongoing malaria control efforts. To prevent the reemergence of the destroyed habitats, S&D vector control officers were trained in habitat surveillance. This research revealed that investment in community health systems enables the longevity of AI-ML real time malaria control by training local officials in habitat identification, treatment, and surveillance. This ensures continual vector density suppression in combination with facilitating program sustainability in an entomological, study site, and intervention area. We have set out to achieve four aims with the completion of the LSM program. Our first aim is to evaluate the efficiency of S&D real-time tools to detect mosquito breeding habitats and deliver optimal microbial loads to suppress the development of malaria mosquitoes in different spatio temporal landscapes. Our second aim is to conduct field surveys in control versus intervention settings to evaluate the impact of combining real time S&D with ongoing malaria control and elimination strategies. The third aim is to construct data-informed transmission models coupled with the effectiveness and costs of AI-ML S&D-driven malaria control strategies to determine the optimal mix of interventions using S&D to eliminate malaria in different endemic settings. The final aim is to develop a protocol to institute S&D alongside ongoing control measures with the Ministry of Health to guide elimination, monitoring, and decision-making.