The　kidney　is　an　abdominal　organ　in　the　human　body　that　supports　filtering　excess　water　and　waste　from　the　blood.　Kidney　diseases　generally　occur　due　to　changes　in　certain　supplements,　medical　conditions,　obesity,　and　diet,　which　causes　kidney　function　and　ultimately　leads　to　complications　such　as　chronic　kidney　disease,　kidney　failure,　and　other　renal　disorders.　Combining　patient　metadata　with　computed　tomography　(CT)　images　is　essential　to　accurately　and　timely　diagnosing　such　complications.　Deep　Neural　Networks　(DNNs)　have　transformed　medical　fields　by　providing　high　accuracy　in　complex　tasks.　However,　the　high　computational　cost　of　these　models　is　a　significant　challenge,　particularly　in　real-time　applications.　This　paper　proposed　SpinalZFNet,　a　hybrid　deep　learning　approach　that　integrates　the　architectural　strengths　of　Spinal　Network　(SpinalNet)　with　the　feature　extraction　capabilities　of　Zeiler　and　Fergus　Network　(ZFNet)　to　classify　kidney　disease　accurately　using　CT　images.　This　unique　combination　enhanced　feature　analysis,　significantly　improving　classification　accuracy　while　reducing　the　computational　overhead.　At　first,　the　acquired　CT　images　are　pre-processed　using　a　median　filter,　and　the　pre-processed　image　is　segmented　using　Efficient　Neural　Network　(ENet).　Later,　the　images　are　augmented,　and　different　features　are　extracted　from　the　augmented　CT　images.　The　extracted　features　finally　classify　the　kidney　disease　into　normal,　tumor,　cyst,　and　stone　using　the　proposed　SpinalZFNet　model.　The　SpinalZFNet　outperformed　other　models,　with　99.9%　sensitivity,　99.5%　specificity,　precision　99.6%,　99.8%　accuracy,　and　99.7%　F1-Score　in　classifying　kidney　disease.　[GRAPHICS]　.